(TITLE OF THE THESIS)* Profiles/Wang_Xiang... · 2018-07-14 · 1.2 Economic Burden of Mental...

THE ACUTE EFFECTS OF AMBIENT TEMPERATURE EXPOSURE ON

MENTAL ILLNESS RELATED EMERGENCY ROOM VISITS IN THE

CITY OF TORONTO.

by

Xiang Wang

A thesis submitted to the Department of Community Health and Epidemiology.

In conformity with the requirements for

the degree of Masters of Science

Queen’s University

Kingston, Ontario, Canada

(May 2013)

Copyright ©Xiang Wang, 2013

ii

Abstract

Objectives: The purpose of this study was to assess the effects of extreme ambient temperature on

hospital emergency room (ER) visits related to mental and behavior disorders in Toronto, Canada.

Methods: A time series study was conducted using health and climatic data from April 1st 2002 to March

31st 2010. Relative risks for increases in ER visits were estimated for specific mental and behavior

disorders (MBD) after exposure to hot and cold temperatures while using 50th percentile of the mean

temperature distribution as the reference. The non-linear nature of the exposure–outcome relationship was

accounted for using a distributed lag non-linear model (DLNM). The effects of seasonality, humidity, day

of the week and outdoor air pollutants (CO2, O3, PM2.5, NO2, and SO2) were also adjusted.

Results: We observed positive associations between elevated mean temperatures and hospital ER visits

for MBD. For hot temperatures, significant increases in ER visits for MBD were observed after a mean

temperature threshold of about 24°C. The association generally lasted about 3 to 4 lag days with the

strongest effect occuring at lag 0 (RR = 1.06; 95% CI: 1.03 - 1.09). Similar trends and associations were

observed for specific mental illnesses such as mood, neurotic, substance abuse, and schizophrenia related

disorders. Cold temperature associations were only observed for schizophrenia.

Conclusions: Our findings suggest that extreme temperature poses a risk to the health and wellbeing for

individuals with mental and behavior disorders. Patient management and education may need to be

improved as extreme temperatures become more prevalent.

iii

Co-Authorship

This research was conducted by Xiang Wang, under the supervision of Dr. Eric Lavigne and Dr.

Hélène-Ouellette-Kuntz. The study protocol was developed by Xiang Wang with feedback from Dr.

Lavigne and Dr. Ouellette-Kuntz. Data collection was developed and collected by Xiang Wang with the

help of Dr. Lavigne. All data analyses were conducted by Xiang Wang with help from Dr. Lavigne. Dr.

Bingchu Chen provided some proofreading assistance for the methods.

iv

Acknowledgements

This work could not have been completed without the advice and support from: Eric Lavigne, Rose

Dugandzic, Antonio Gasparrini, Hélène Ouellette-Kuntz, and Harriet Richardson. Also I would like to

thank all my colleagues at the Public Health Agency of Canada/Environmental Issues Division and the

Leslie Dan Faculty of Pharmacy University of Toronto for granting me a deferral to finish this work. I

would also like to thank my two best friends Erin Liu and George Huang for their support and friendship.

Finally, I would like to thank my parents for many years of nurture and love.

v

Table of Contents

Abstract ......................................................................................................................................................... ii

Co-Authorship .............................................................................................................................................. iii

Acknowledgements ...................................................................................................................................... iv

List of Figures...............................................................................................................................................vii

List of Tables...............................................................................................................................................viii

Chapter 1 General Introduction ..................................................................................................................... 1

1.1 Epidemiology of Mental Illness in Canada ......................................................................................... 1

1.2 Economic Burden of Mental Illness .................................................................................................... 3

1.3 Mental Health Service Utilization in Ontario ...................................................................................... 3

1.4 Barriers to Mental Health Utilization in Ontario ................................................................................. 4

1.5 Relationship between Ambient Temperature and Morbidity .............................................................. 5

1.6 Relationship between Ambient Temperature and Mental Illness: Biological Plausibility .................. 5

1.7 Purpose and Rationale ......................................................................................................................... 6

1.8 References ........................................................................................................................................... 8

Chapter 2: The Impact of Ambient Temperature on Mental Illness related Mortality and Morbidity: A

Systematic Review. ..................................................................................................................................... 15

2.1 Abstract ............................................................................................................................................. 16

2.2 Introduction ....................................................................................................................................... 17

2.3 Methods ............................................................................................................................................. 18

2.4 Results ............................................................................................................................................... 19

2.4.1 Ambient Temperature and Mental Illness Related Morbidity .................................................... 21

2.4.2 Ambient Temperature and Mental Illness Related Mortality ..................................................... 23

2.5 Discussion ......................................................................................................................................... 26

2.5.1 Exposure Assessment ................................................................................................................. 27

2.5.2 Outcome Measurements ............................................................................................................. 28

2.5.3 Statistical Methods ..................................................................................................................... 30

2.6 Suggestions for Future Research ....................................................................................................... 30

2.7 References ......................................................................................................................................... 31

2.8 Tables ................................................................................................................................................ 36

Chapter 3: Acute Impacts of Extreme Temperature Exposure on Emergency Room Visits Related to

Mental and Behavior Disorders in the City of Toronto, Canada. ................................................................ 55

3.1 Abstract ............................................................................................................................................. 56

vi

3.2 Introduction ....................................................................................................................................... 57

3.3 Methods ............................................................................................................................................. 58

3.3.1 Study Population and Morbidity Data ........................................................................................ 58

3.3.2 Weather and Air Pollution Data ................................................................................................. 58

3.3.3 Time Series Dataset .................................................................................................................... 59

3.3.4 Statistical Methods ..................................................................................................................... 59

3.3.5 Potential Confounders ................................................................................................................ 61

3.4 Results ............................................................................................................................................... 61

3.4.1 Ambient Temperature and Environment Characteristics ........................................................... 61

3.4.2 Distributed Effects of Ambient Temperature on Emergency Room Visits ................................ 64

3.4.3 Mental and Behavior Disorders (MBDs) (ICD-10 F00-F99) ..................................................... 67

3.4.4 MBDs due to psychoactive substance abuse (ICD-10 F10-F19) ............................................... 69

3.4.5 Schizophrenia, Schizotypal and Delusional Disorders (ICD-10 F20-29) .................................. 70

3.4.6 Mood Disorders (ICD 10 F30-F39) ............................................................................................ 71

3.4.7 Neurotic Disorders (ICD 10 F40-F49) ....................................................................................... 71

3.5 Discussion ......................................................................................................................................... 72

3.6 Conclusions ....................................................................................................................................... 78

3.7 References ......................................................................................................................................... 79

Chapter 4 General Discussion and Conclusions .......................................................................................... 84

4.1 Summary of Findings ........................................................................................................................ 84

4.1 Limitations ........................................................................................................................................ 84

4.1.1 Exposure misclassification ......................................................................................................... 85

4.1.2 Outcome misclassification.......................................................................................................... 86

4.1.3 Statistical Challenges ................................................................................................................. 88

4.2 Public Health Implications ................................................................................................................ 89

4.3 References ......................................................................................................................................... 90

4.4 Appendices ........................................................................................................................................ 92

4.4.1 Appendix A: Ethics Certificate .................................................................................................. 92

4.4.2 Appendix B: Example of Time Series Dataset, ER Visit Distribution over a year, and

Temperature Distribution over a year .................................................................................................. 93

4.4.3 Appendix C: Statistical Model Equation .................................................................................... 96

vii

List of Figures

Figure 2-1 Flowchart of Literature Search Results ..................................................................................... 20

Figure 3-1 Cumulative Effect of Ambient Temperature on Mental and Behavior Illnesses-Related ER

Visits............................................................................................................................................................ 68

Figure 3-2 Lagged Effects of Mean Ambient Temp on MBD based on age humidity levels. .................... 69

Figure 3-3 Cumulative Effects of Mean Temp on Various Mental and Behavior Disorders ...................... 70

viii

List of Tables

Table 2-1 Descriptive Statistics of Included Studies ................................................................................... 37

Table 2-2 Effect Estimates of Included Studies .......................................................................................... 47

Table 3-1 Descriptive Statistics ................................................................................................................... 65

Table 3-2 Correlation Coefficients .............................................................................................................. 65

Table 3-3 Distributed Lag Effects of Ambient Temperature on various Mental Illnesses .......................... 65

1

Chapter 1

General Introduction

In this section, background information about the rates and burden of mental illness in

Canada will be presented. A section will be devoted to describe the healthcare resource utilization

of patients with mental illness and some barriers to access. Subsequently, the biological

relationship between ambient temperature and health morbidities will be presented. Finally, the

current research on the relationship between ambient temperature and mental illness will be

summarized followed by a rationale for this study.

1.1 Epidemiology of Mental Illness in Canada

Mental illness is a health condition characterized by significant dysfunction in an

individual’s cognitions, emotions, or behaviors that reflects a disturbance in the psychological,

biological, or developmental processes underlying mental functioning (Ang et al. 2004). Mental

illnesses take on several forms such as mood disorders, schizophrenia, anxiety disorders,

personality disorders, eating disorders, substance abuse, and gambling addictions. The burden of

mental illness is significant. It affects individuals with various levels of education and income,

from different cultures, with different occupations, of different ages, gender, and geographical

locations.

According to the Report on Mental Illness in Canada (Health Canada, 2002),

approximately one in five Canadian adults will have experienced a mental illness at some point

during their lifetime and over 70% of all mental illness will occur during childhood or

adolescence. The Human Face of Mental Health and Mental Illness in Canada (Public Health

2

Agency of Canada, 2006) reports that individuals 14 to 25 years of age are most likely to report a

mental illness and Canadians with the lowest income will be three to four times more likely to

experience a mental illness compared to individuals in the highest income group.

The most recent Canadian Mental Health and Wellbeing survey (CCHS1.2) (Public

Health Agency of Canada, 2006) revealed that one out of every 10 Canadians aged 15 and over

reports symptoms correlated with mood, anxiety, and substance dependence issues. Some of the

most prevalent forms of mental illness among Canadians aged 15 and over are: mood disorders

(~4.9%), anxiety disorders (~4.9%), schizophrenia (~1%), and substance abuse disorders

(~3.6%). In addition, the Canadian Study of Health and Aging found that the rates of neuro-

degenerative disease such as dementia among Canadian seniors aged 65 and over is ~ 8% and for

individuals 85 and over, the rate is ~ 34.5%.

For the affected individual, mental illness can result in loss of productivity, limitation to

career opportunities, loss of earning, and increased risk of suicide (Health Canada, 2002). In

addition, recent research (Himelhoch et al. 2004, McIntyre et al. 2006) indicates that having a

mental illness can increase the individual’s risk of acquiring certain cardiovascular and

respiratory conditions. For the affected family, the burden of mental illness can be significant as

there are numerous difficulties they may face such as coping and making decisions regarding

treatment, housing, and hospitalization (Pinquart and Sorensen 2007, Pinquart and Sorenson

2003). Primary and secondary caregivers, have an increased risk of psychological distress, loss of

productivity, increased financial burden, impaired health habits, physical illness, mental illness

such as depression, and in some extreme cases death (Vitaliano et al. 2003, Pinquart and

Sorenson 2003, Schulz and Sherwood 2008).

3

1.2 Economic Burden of Mental Illness

In Canada, the economic burden of mental illness on society is considerable. Studies

which consider only the direct medical cost of mental illness have reported estimates of ~ $5.7

billion annually on the health care system (Jacobs et al. 2008). Recent studies adopting a societal

perspective (economic evaluations which considers both direct medical costs and loss of income

to the individual) reported that the primary cost of mental illness stems from short term and long

term loss of productivity in the workplace and losses in health related quality of life years

(HRQOL) resulting in costs of $51 billion annually (Lim et al. 2008) to society.

1.3 Mental Health Service Utilization in Ontario

The Mental Health and Wellbeing survey (CCHS Cycle 1.2) reports that in Ontario, on

average ~8.7% of the individuals (aged 15+) used a health care provider for reason of mental

illness during a one year period. With respect to utilization by health care provider type, ~5.3%

of individuals (aged 15+) visited general practitioners as their initial point of contact for reasons

due to mental illness. 2.2% and 2.0% of individuals (aged 15+) with mental illness had a

psychiatrist or a psychologist as their first point of contact. Finally, ~1.6% of the individuals

(aged 15+) sought care from social workers as their initial point of contact for reasons related to

mental illness. In addition, hospital emergency departments may represent the first point of

contact for individuals with mental illness requiring immediate medical intervention. According

to the report: Exploring Hospital Mental Health Service Use in Ontario, the rate of

hospitalization related to mental illness in Ontario was 608 admissions per 100,000 (Canadian

Institute of Health Information, 2008). Schizophrenia, mood disorders, anxiety disorders, and

4

substance abuse issues accounted for over 85% of hospital admissions (Canadian Institute of

Health Information, 2008).

1.4 Barriers to Mental Health Utilization in Ontario

Despite the broad spectrum of health care services offered to patients with mental illness,

there are still significant barriers to mental health service utilization in Ontario. Currently, a

number of attitudinal and structural barriers exist for access to mental health services. Sareen et

al. (2007) conducted a survey among individuals with a diagnosed mental illness in Ontario to

examine the perceived attitudinal and structural barriers for access to health care services. The

study revealed that individuals with mental illness do not seek help from healthcare services due

to: (1) a perception that the problem would resolve on its own (46%), (2) a desire to resolve the

issue by themselves due to privacy concerns (15%), (3) a feeling that seeking help would not do

any good (23%), (4) a fear of being hospitalized against their will (6%), (5) a concern for what

others might think (15%), and (6) a lack of satisfaction with the available services (10%). In

addition, Ontarians identified structural barriers to accessing mental health services, including

the lack of insurance coverage for services not covered under OHIP (14%), the loss of

productivity from too much time spent on treatment (19%), not being able to get an appointment

(4%), and being unsure where to go to seek help (18%). Similar results have been reported by

Mackenzie et al. (2006) who also conducted a survey among the general population regarding

perceived barriers to care for patient with mental illness. There are also age and gender

differences regarding mental health service utilization. Men are less likely compared to women to

seek mental health services (Addis and Mahalik 2003) Also, some studies have indicated that

older adults are more sensitive to the stigmatization associated with mental illness compared to

5

the younger generation (Currin et al. 1998, Robb et al. 2003, Berger et al. 2008, Sirey et al.

2001).

1.5 Relationship between Ambient Temperature and Morbidity

The effect of ambient temperature on morbidity in recent years is arousing significant

public health interest due to steady increases in global ambient temperature. Increases in daily

ambient temperature are associated with increased daily morbidity based on hospital admission

data (Ye et al. 2012, Schwartz et al. 2004, Michelozzi et al. 2009). Both extreme hot and cold

temperatures can have adverse effects on human health. The effects of hot temperatures on

morbidity are primarily found within a lag period of 0 to 5 days while the lag period for cold

temperatures are generally longer, up to periods of two weeks (Gosling et al. 2008, Conti et al.

2005, Green et al. 2010). The increased risk of hospital admissions for cardiovascular,

respiratory, and cerebrovascular outcomes with exposure to increased ambient temperature above

a specific threshold has been demonstrated (Lin et al. 2009, Panagiotakos et al. 2004, Schwartz et

al.2004, Michelozzi et al. 2009, Dawson et al. 2008, Wang et al. 2012). A recent systematic

review and meta- analysis of 21 studies reported pooled effect estimates of 2.0% increase (95%

confidence interval: 1.4% to 5.5%) in respiratory morbidity after 1 °C increase in temperature

(Turner et al. 2012). Turner et al. (2012) also reported no effect of temperature on cardiovascular

morbidities when all 21 studies were pooled.

1.6 Relationship between Ambient Temperature and Mental Illness: Biological Plausibility

Thermoregulation is the process where the body maintains its steady state temperature

when a change in the external environment occurs. The main regulating center for human

thermoregulation resides in the anterior hypothalamus and pre-optic region (Boulant 2000,

6

Ishiwata et al. 2002, Hasegawa et al. 2005). The pathophysiologies of some mental illnesses (i.e.,

schizophrenia) have been shown to induce alterations in central dopamine neurotransmission

which is associated with human thermoregulation through its interactions with the hypothalamic

regions of the brain (Crandall et al. 2002, Kapur and Remington 1996, Shiloh et al. 2001,

Lindvall et al. 1983). These alterations may lead to epigenetic changes such as imprinting, DNA

methylation, and histone acetylation which are associated with thermo-regulatory dysfunction

(Shiloh et al. 2001). These theories are supported by studies which report abnormal thermo-

regulation (i.e., hyperthermia and hypothermia) in both drug free and medicated patients with

mental illnesses (Chong and Castle 2004, Hermesh et al. 2000, Schwaninger et al. 1998).

Although, some animal studies (Yuan et al. 2006, Wiste et al. 2008) have demonstrated the

impaired dopaminergic transmission empirically, the plausible link between impaired

dopaminergic transmission and impaired thermoregulation has not been conclusively proven.

Also there is some literature suggesting possible exacerbation of the hazardous psychiatric effect

by air pollution (Lundberg et al.1996); however, the link has only been theorized and not

investigated empirically.

1.7 Purpose and Rationale

Given the public health importance of mental illness and climatic changes experienced

around the globe, the thesis will seek to illustrate , at a population level, the relationship

between ambient temperature and mental illness and how best to study this relationship. The

thesis includes a systematic review of the literature linking ambient temperature to mental illness

morbidity and mortality (Chapter 2 – Manuscript 1) and a study of the association between

7

ambient temperature and daily mental illness related hospital emergency visits (Chapter 3 –

Manuscript 2).

8

1.8 References

1. Addis, M.E., Mahalik J.R., 2003. Men, masculinity, and the contexts of help seeking. Am

Psychol. 58,5-14.

2. Allen G., 2010. Evaluation of Transformation Methods for Adjustment of Historical

TEOM® Data in the NAPS Network. Environment Canada.

3. American Psychiatric Association (2000). Diagnostic and Statistical Manual of Mental

Disorders (4th

ed., text revision). Washington, DC: American Psychiatric Association.

4. Ang, R.P., Lim, K.M., Tan, A., Yau, T.Y., 2004. Effects of gender and sex role

orientation on help-seeking attitudes. Current Psychology.23,203-14.

5. Berger, J.M., Levant, R.F., McMillan, K,K., Kelleher, W., Sellers, A., 2008. Impact of

gender role conflict, traditional masculinity ideology, alexithymia, and age on men's

attitudes toward psychological help seeking. Psychology of Men & Masculinity. 9, 192-

94.

6. Boulant, J.A., 2000. Role of the preoptic-anterior hypothalamus in thermoregulation and

fever. Clin. Infect. Dis. 31, 157-61.

7. Butler-Jones, D. (2011). The Chief Public Health Officer’s Report on the State of Public

Health in Canada, 2011: Growing Up Well – Priorities for a Healthy Future. (Ottawa:

Public Health Agency of Canada).

8. Chong, T.W. and Castle, D.J., 2004. Layer upon layer: thermoregulation in schizophrenia

Schizophr. Res. 69, 149-157.

9. Conti, S., Meli, P., Minelli, G., Solimini, R., Toccaceli, V., Vichi, M., Beltrano, C.,

Perini, L., 2005. Epidemiologic study of mortality during the summer 2003 heat wave in

Italy. Environ. Res. 98, 390-399.

10. Crandall, C.G., Vongpatanasin, W., Victor, R.G., 2002. Mechanism of cocaine-induced

hyperthermia in humans. Ann. Intern. Med. 136, 785-791.

11. Currin, J.B., Hayslip, B., Schneider, L.J., Kooken, R.A., 1998. Cohort differences in

attitudes toward mental health services among older persons. Psychotherapy: Theory,

Research, Practice, Training. 35, 506-18.

12. Dawson, J., Weir, C., Wright, F., Bryden, C., Aslanyan, S., Lees, K., et al. 2008.

Associations between meteorological variables and acute stroke hospital admissions in the

west of Scotland. Acta. Neurol. Scand. 117, 85-9.

9

13. Exploring Hospital Mental Health Service Use in Ontario, 2007-2008. Ottawa, Canadian

Institute of Health Information.

14. Gasparrini, A., 2011. Distributed Lag Linear and Non-Linear Models in R: The Package

dlnm. J. Stat. Softw. 43, 1-20.

15. Gasparrini, A. and Armstrong, B., 2010. Time series analysis on the health effects of

temperature: Advancements and limitations. Environ. Res. 110, 633-638

16. Gasparrini, A., Armstrong, B., Kenward, M.G., 2010. Distributed lag non-linear models.

Stat. Med. 29, 2224-2234.

17. Goldberg, M.S., Gasparrini, A., Armstrong, B., Valois, M.F., 2011. The short-term

influence of temperature on daily mortality in the temperate climate of Montreal, Canada.

Environ. Res.

18. Gosling, S.N., Lowe, J.A., McGregor, G.R., Pelling, M., Malamud, B.D., 2009.

Associations between elevated atmospheric temperature and human mortality: a critical

review of the literature. Clim. Change. 92, 299-341.

19. Green, R.S., Basu, R., Malig, B., Broadwin, R., Kim, J.J., Ostro, B., 2010. The effect of

temperature on hospital admissions in nine California counties. Int J Public Health. 55,

113-21.

20. Gulliver, A., Griffiths, K.M., Christensen, H., Brewer, J.L., 2012. A systematic review of

help-seeking interventions for depression, anxiety and general psychological distress.

BMC Psychiatry. 12, 81-7.

21. Hansen, A., Bi, P., Nitschke, M., Ryan, P., Pisaniello, D., Tucker, G., 2008. The effect of

heat waves on mental health in a temperate Australian city Environ. Health Perspect. 116,

1369-1375.

22. Hasegawa, H., Ishiwata, T., Saito, T., Yazawa, T., Aihara, Y., Meeusen, R., 2005.

Inhibition of the preoptic area and anterior hypothalamus by tetrodotoxin alters

thermoregulatory functions in exercising rats. J. Appl. Physiol. 98, 1458-1462.

23. Health Canada .Report on Mental Illness in Canada 2002. (Ottawa: Health Canada).

10

24. Hermesh, H., Shiloh, R., Epstein, Y., Manaim, H., Weizman, A., Munitz H., 2000.Heat

intolerance in patients with chronic schizophrenia maintained with antipsychotic drugs.

Am J Psychiatry; 157:1327-9.

25. Himelhoch, S., Lehman, A., Kreyenbuhl, J., Daumit, G., Brown, C., Dixon, L., 2004.

Prevalence of chronic obstructive pulmonary disease among those with serious mental

illness. Am. J. Psychiatry. 161, 2317-2319.

26. Human face of Mental Health and Mental Illness in Canada. 2006. (Ottawa: Public Health

Agency of Canada).

27. Ishiwata, T., Hasegawa, H., Yazawa, T., Otokawa, M., Aihara, Y., 2002. Functional role

of the preoptic area and anterior hypothalamus in thermoregulation in freely moving rats.

Neurosci Lett. 325,167-70.

28. Jacobs, P., Yim, R., Ohinma, A., Eng, K., Dewa, CS., Bland, R., et al. 2008.

Expenditures on mental health and addictions for Canadian provinces in 2003 and 2004.

Can J Psychiatry.53,306-13.

29. Kapur, S. and Remington, G., 1996. Serotonin-dopamine interaction and its relevance to

schizophrenia. Am. J. Psychiatry. 153, 466-476.

30. Kovats, R.S. and Kristie, L.E., 2006. Heatwaves and public health in Europe . Eur J

Public. Health. 16,592-9.

31. Lim, K.L., Jacobs, P., Ohinmaa, A., Schopflocher, D., Dewa, C.S., 2008. A new

population-based measure of the economic burden of mental illness in Canada. Chronic

Dis Can. 28, 92-8.

32. Lin, S., Luo, M., Walker, R.J., Liu, X., Hwang, S.A., Chinery, R., 2009. Extreme high

temperatures and hospital admissions for respiratory and cardiovascular diseases.

Epidemiology. 20, 738-746.

33. Lin, Y., Chang, C., Li, M., Wu, Y., Wang, Y., 2012. High-temperature indices associated

with mortality and outpatient visits: Characterizing the association with elevated

temperature. Sci. Total Environ. 427-428, 41-49.

34. Lindvall, O., Bjorklund, A., Skagerberg, G., 1983. Dopamine-containing neurons in the

spinal cord: anatomy and some functional aspects. Ann Neurol. 14, 255-60.

11

35. Lundberg, A., 1996. Psychiatric aspects of air pollution. Otolaryngol. Head. Neck. Surg.

114, 227-231.

36. Mackenzie, C.S., Gekoski, W.L., Knox, V.J., 2006. Age, gender, and the underutilization

of mental health services: the influence of help-seeking attitudes. Aging Ment Health. 10,

574-82.

37. McCullagh P. and Nelder, J. A. (1989) Generalized Linear Models. London: Chapman

and Hall.

38. McIntyre, R.S., Konarski, J.Z., Soczynska, J.K., Wilkins, K., Panjwani, G., Bouffard, B.,

Bottas, A., Kennedy, S.H., 2006. Medical comorbidity in bipolar disorder: implications

for functional outcomes and health service utilization. Psychiatr. Serv. 57, 1140-1144.

39. McMichael, A.J., Woodruff, R.E., Hales, S., 2006. Climate change and human health:

present and future risks. Lancet. 367, 859-869.

40. Michelozzi, P., Accetta, G., De Sario, M., D'Ippoliti, D., Marino, C., Baccini, M., Biggeri,

A., Anderson, H.R., Katsouyanni, K., Ballester, F., Bisanti, L., Cadum, E., Forsberg, B.,

Forastiere, F., Goodman, P.G., Hojs, A., Kirchmayer, U., Medina, S., Paldy, A.,

Schindler, C., Sunyer, J., Perucci, C.A., PHEWE Collaborative Group, 2009. High

temperature and hospitalizations for cardiovascular and respiratory causes in 12 European

cities. Am. J. Respir. Crit. Care Med. 179, 383-389.

41. Oke, T.R., 1982. The energetic basis of the urban heat island. Q J R Meteorol Soc. 108, 1-

24.

42. Panagiotakos, D.B., Chrysohoou, C., Pitsavos, C., Nastos, P., Anadiotis, A., Tentolouris,

C., 2004. Climatological variations in daily hospital admissions for acute coronary

syndromes. Int J Cardiol. 94, 229-33.

43. Pinquart, M., Sorensen, S., 2007. Correlates of physical health of informal caregivers: a

meta-analysis. J Gerontol B Psychol Sci. 62, 126-37.

44. Pinquart, M., Sorensen, S., 2003. Differences between caregivers and noncaregivers in

psychological health and physical health: a meta-analysis. Psychol Aging. 18, 250-67.

12

45. Robb, C., Haley, W.E., Becker, M.A., Polivka, L.A., Chwa, H.J., 2003. Attitudes towards

mental health care in younger and older adults: similarities and differences. Aging Ment

Health. 7,142-52.

46. Sareen, J., Jagdeo, A., Cox, B.J., Clara, I., Have, M., Belik, S.L., 2007. Perceived barriers

to mental health service utilization in the United States, Ontario, and the Netherlands.

Psychiatr Serv. 58,357-64.

47. Schulz, R., Sherwood, P.R., 2008. Physical and mental health effects of family

caregiving. Am J Nurs. 108, 7-27.

48. Schwaninger, M., Weisbrod, M., Schwab, S., Schroder, M., Hacke, W., 1998.

Hypothermia induced by atypical neuroleptics. Clin. Neuropharmacol. 21, 344-346.

49. Schwartz, J., Samet, J.M., Patz, J.A., 2004. Hospital admissions for heart disease: the

effects of temperature and humidity. Epidemiology. 15,755-61.

50. Shalev, A., Hermesh, H., Munitz, H., 1988. The role of external heat load in triggering the

neuroleptic malignant syndrome. Am J Psychiatry. 145,110-1.

51. Shiloh, R., Hermesh, H., Weizer, N., Dorfman-Etrog, P., Weizman, A., Munitz, H., 2000.

Acute antipsychotic drug administration lowers body temperature in drug-free male

schizophrenic patients. European Neuropsychopharmacology. 10, 443-445.

52. Shiloh, R., Shapira, A., Potchter, O., Hermesh, H., Popper, M., Weizman, A., 2005.

Effects of climate on admission rates of schizophrenia patients to psychiatric hospitals.

Eur. Psychiatry. 20, 61-64.

53. Shiloh, R., Weizman, A., Epstein, Y., Rosenberg, S.L., Valevski, A., Dorfman-Etrog, P.,

Wiezer, N., Katz, N., Munitz, H., Hermesh, H., 2001. Abnormal thermoregulation in

drug-free male schizophrenia patients. Eur. Neuropsychopharmacol. 11, 285-288.

54. Sirey, J.A., Bruce, M.L., Alexopoulos, G.S., Perlick, D.A., Raue, P., Friedman, S.J., 2001.

Perceived stigma as a predictor of treatment discontinuation in young and older

outpatients with depression. Am J Psychiatry. 158, 479-81.

13

55. Sung, T., Chen, M. Lin, C. Lung, S., Su, H., 2011. Relationship between mean daily

ambient temperature range and hospital admissions for schizophrenia: Results from a

national cohort of psychiatric inpatients. Sci. Total Environ. 410-411, 41-46.

56. Turner, L.R., Barnett, A.G., Connell, D., Tong, S., 2012. Ambient Temperature and

Cardiorespiratory Morbidity: A Systematic Review and Meta-analysis. Epidemiology. 23,

594-606.

57. Vasiliadis, H.M., Lesage, A., Adair, C., Boyer, R., 2005. Service use for mental health

reasons: cross-provincial differences in rates, determinants, and equity of access. Can J

Psychiatry. 50, 614-9.

58. Vitaliano, P.P., Zhang, J., Scanlan, J.M., 2003. Is caregiving hazardous to one's physical

health? A meta-analysis. Psychol Bull. 129, 946-72.

59. Wang, X.L., Yang, L., Chan, K.P., Chiu, S.S., Chan, K.H., Peiris, J.S., 2012. Model

selection in time series studies of influenza-associated mortality. PLoS One 7, e39423.

60. Wang, X.Y., Barnett, A.G., Yu, W., FitzGerald, G., Tippett, V., Aitken, P., Neville, G.,

McRae, D., Verrall, K., Tong, S., 2012. The impact of heatwaves on mortality and

emergency hospital admissions from non-external causes in Brisbane, Australia. Occup.

Environ. Med. 69, 163-169.

61. Wang, Y., Lin, Y., Chuang, C., Li, M., Chou, C., Liao, C., 2012. Associating emergency

room visits with first and prolonged extreme temperature event in Taiwan: A population-

based cohort study. Sci Total Environ. 416, 97-104.

62. Williams, S., Nitschke, M., Sullivan, T., Tucker, G.R., Weinstein, P., Pisaniello, D.L.,

Parton, K.A., Bi, P., 2011. Heat and health in Adelaide, South Australia: Assessment of

heat thresholds and temperature relationships. Sci. Total Environ. 414, 126-33.

63. Williams, S., Nitschke, M., Weinstein, P., Pisaniello, D.L., Parton, K.A., Bi, P., 2012. The

impact of summer temperatures and heatwaves on mortality and morbidity in Perth,

Australia. 1994–2008. Environ. Int. 40, 33-38.

64. Wiste, A.K., Arango, V., Ellis, S.P., Mann, J.J., Underwood, M.D., 2008. Norepinephrine

and serotonin imbalance in the locus coeruleus in bipolar disorder. Bipolar Disord. 10,

349-59.

14

65. Ye, X., Wolff, R., Yu, W., Vaneckova, P., Pan, X., Tong, S., 2012. Ambient temperature

and morbidity: a review of epidemiological evidence. Environ. Health Perspect. 120, 19-

28.

66. Yuan, J., Hatzidimitriou, G., Suthar, P., Mueller, M., McCann, U., Ricaurte, G., 2006.

Relationship between temperature, dopaminergic neurotoxicity, and plasma drug

concentrations in methamphetamine-treated squirrel monkeys. J. Pharmacol. Exp. Ther.

316, 1210-1218.

67. Zeger, S.L., Irizarry, R., Peng, R.D., 2006. On time series analysis of public health and

biomedical data. Annu Rev Public Health. 27,57-79.

15

Chapter 2

The Impact of Ambient Temperature on Mental Illness related Mortality and

Morbidity: A Systematic Review.

16

2.1 Abstract

Objectives: The purpose of this study was to summarize the current literature between extreme ambient

temperature exposure and mental illness morbidity and mortality.

Methods: A systematic review of the current literature was undertaken to identify relevant studies

examining the effect of ambient temperature on mental illness or health related morbidity and mortality.

Studies investigating the effects of ambient temperature on mental illness morbidity and mortality

published up to August 12th 2012 were retrieved using the following electronic databases: PubMed

MEDLINE, EMBASE, Web of Science, Scopus and PsycINFO.

Results: 15 studies examining the relationship between ambient temperature exposure and mental illness

related morbidity and mortality were reviewed. All studies reported statisically significant increases in ER

visits, hospital admissions, mortality, and ambulance call outs for individuals with mental illness during

periods of elevated ambient temperature. Studies examining cold temperature associations were not found.


individuals with mental and behaviour illnesses. Further research is needed which accounts for the lagged

structure of these exposure–outcome relationships and adjustw for relevant confounders.

17

2.2 Introduction

The effect of ambient temperature on morbidity in recent years is arousing significant

public health interest due to steady increases in global ambient temperature. Increases in daily

ambient temperature have been shown to be associated with increased daily morbidity and

mortality (Ye et al. 2011, Michelozzi et al. 2009). Both extreme hot and cold temperatures can

have adverse effects on human health. The effects of hot temperatures on morbidity and mortality

are primarily found within a lag period of 0 to 5 days while the lag period for cold temperatures

are generally longer, up to periods of two weeks (Gosling et al. 2009, Conti et al. 2005). Current

literature has demonstrated the increased risk of hospital admissions and mortality for

cardiovascular, respiratory, and cerebrovascular outcomes with exposure to increased ambient

temperature above a specific threshold (Lin et al. 2009, Schwartz 2004, Michelozzi et al. 2009,

and Wang et al. 2012).

While the association between ambient temperature and cardiovascular and respiratory

illnesses has been well investigated, there is limited evidence indicating that individuals with

mental illness may be at significantly higher risk of hospital admission, emergency visits and

mortality during periods of extreme high and low ambient temperature. In contrast with the

general population, individuals with mental illness often experience poorer states of health

(McMichael et al. 2006). It has also been well established that the use of psychotropic medication

interferes with thermoregulation (Martin-Latry et al. 2007). Thus, individuals with mental illness

are highly susceptible to the effects of extreme ambient temperature. Recently, there has been

increasing interest in assessing the impact of ambient temperature on individuals with mental

illness as significant fluctuations in ambient temperatures are becoming more prevalent.

18

However, to date, no review has specifically focused on the relationship between temperature and

mental illness. Thus, we will try to fill this knowledge gap and illustrate the effects of ambient

temperature on mental illness morbidity and mortality.

2.3 Methods

A systematic review of the current literature was undertaken to identify relevant studies

examining the effect of ambient temperature on mental illness or health related morbidity and

mortality. Studies investigating the effects of ambient temperature on mental illness morbidity

and mortality published up to August 12th

2012 were retrieved using the following electronic

databases: PubMed MEDLINE, EMBASE, Web of Science, Scopus and PsycINFO. Limitations

were applied to include only peer-reviewed full English journal articles. The reference lists of

identified papers were searched to make sure all relevant papers were captured.

Adults aged 18+ were the targeted population of this review. The primary search strategy

used the following U.S. National Library of Medicine’s Medical Subject Headings (MeSH

Terms) and keywords: “Temperature”, “Ambient Temperature”, “Heat Waves”, “Heat wave”,

“Mental Illness”, “Mental Disorder”, “Cold Weather”, “ Cold Temperature”, “Cold Spells”,

“Psychiatric Illness”, “Emergency Admissions”, “Emergency Visits”, “Hospital Admissions”,

“Hospitalization”, “Mortality”, and “Morbidity”.

The title and abstract were initially used to exclude studies not related to the research

question. The remaining results were merged into a refworks account and duplicates were

removed. Full texts of the remaining studies were then reviewed against the inclusion criteria to

determine eligibility.

19

Eligibility was determined based on the study design, the use of original data, the use of

appropriate effect estimates, and the use of relevant exposure and outcomes. Study designs were

required to be case control, time-series, or case crossover. Each study had to include the use and

collection of original data and the presence of appropriate effect estimates (Odds Ratios, Relative

Risk, Percent change in morbidity and mortality due to extreme temperature, and Incidence Rate

Ratio). Furthermore, the main exposure of the study had to be an appropriate indicator /measure

of ambient temperature. Finally, the outcome had to include measures of mental illness related

morbidity (Hospital Admissions and/or Emergency Visits) and/or mortality.

2.4 Results

We identified 1324 papers in the literature search. Based on the inclusion criteria, 15

articles were included in the final review (Figure 2-1). All included studies examined elevated

temperatures. Among the included studies, five examined the effects of temperature related

mental illness mortality; five studies examined effects of temperature on mental illness related

hospital admissions and emergency rooms visits, and five studies examined both temperature

related mental illness mortality and morbidity.

20

Number of articles from

database search: 1,213

Number of articles after

removal of duplicates: 1,035

Number of abstracts screened:

300

Number of full-text articles

evaluated: 32

Number of studies included in

analysis: 15

Number of articles excluded based on

title: 735

Number of articles excluded: 17

Number of articles excluded based on

abstract: 268

Figure 2-1 Flowchart of Literature Search Results

21

2.4.1 Ambient Temperature and Mental Illness Related Morbidity

The studies examining ambient temperature and mental illness morbidity are listed in

Tables 2-1 and 2-2. Nine studies examined the effects of ambient temperature on mental illness

related morbidity. Morbidity was measure in terms of emergency department visits and/or

hospital admissions.

Three studies used a case series study design and six used a time series design.

Statistical analyses were conducted using GLM (General Linear Modeling), GEE (General

Estimation Equations), or GAM (General Additive Models). In some studies, confounding

variables were considered, such as various gaseous pollutants (NO2, CO, O3, and SO2),

particulate matter (PM10, PM2.5), humidity, and different seasons. However, adjustments for these

variables were undertaken by a minority (2) of the studies.

The most commonly used metrics for ambient temperature were daily mean temperature

and daily maximum temperature, although one study used apparent temperature. It is well known

that the potential impacts of ambient temperature are often lagged over a period of several days

for heat effects and up to periods of two weeks for cold effects (Ye et al. 2011). However, none

of the studies considered the lagged effects of the ambient temperature.

Several methods were used to model the relationship between ambient temperature and

mental illness/mental health-related morbidity. Nine studies examined heat effects using either a

linear relationship or a linear relationship incorporating a particular threshold. For specific

temperature thresholds, most studies used >35°C as extreme heat exposure. In the absence of

specific thresholds for temperature, various percentiles were used to evaluate the effects of heat.

Generally, the studies found that exposure to extreme heat had significant effects on

22

mental illness/mental health related morbidity in Australia, England, Taiwan, and the United

States. All of the studies were conducted in high income countries.

In Chicago, Semenza et al. (1999) found that during periods of elevated temperatures, the

number of hospital admissions for several neurological conditions such as Parkinson’s disease

and Alzheimer’s disease significantly increased. In Adelaide, Australia, Nitschke et al. (2007)

reported that during periods where the temperatures was > 35°C, mental illness related hospital

admissions increasingly significantly by 7% across all ages. The greatest increase of 17% was

seen in individuals 75+.

Khalaj et al. (2010) explored the effect of ambient temperature on emergency hospital

admissions during periods of extreme heat in New South Wales, Australia, while adjusting for

various air pollutants (NO2, PM10, and O3). They reported that during periods of extreme

temperature (≥99th

percentile of max and min temp), emergency hospital admissions directly

related to mental and behavior disorders (ICD 10 F00-F99) increased significantly by 4% at lag

0, and 7% using a three day average. In addition, they reported a 7 to 11% increase for

emergency hospital admissions for heat related injuries among individuals with mental illness.

This suggested that mental illness may act as a modifier of the association between extreme heat

events and emergency hospital admissions. Similar results were reported by Williams et al.

(2012a, 2012b).

In Adelaide, Australia, Hansen et al. (2008) explored the effects of heat waves on cause

specific mental illness/disorders/health related hospital admissions during 1993-2006. They

found that above thresholds of 26.7°C, a significant positive association existed between ambient

temperature and hospital admissions for mental and behavior disorders (ICD10 F00-F99).

23

Specifically, hospital admissions significantly increased for organic and symptomatic mental

disorders (F00-F09), dementia (F00-F03), mood (affective) disorders (F30-F39), neurotic stress

related (F40-F48), somatoform (F60-F69), disorder of the psychological development (F80-F89);

and senility (R54). There were no significant differences in hospital admission across strata by

sex and by age.

Sung et al. (2011) evaluated the effects of ambient temperature on schizophrenia

admissions and reported positive associations for temperatures in the 1st and 99th

percentile.

Nitschke et al. (2011) examined the pattern of several years of heat waves on ambulance call

outs, hospital admissions and emergency room presentations. They found that in comparison

with previous heat waves, the risk for ambulance call outs, hospital admissions, and emergency

room admissions for individuals with mental illnesses/disorders have been consistently

decreasing. Finally, Wang et al. (2012) found no significant association across all ages between

ambient temperature and mental illness-related emergency room visits.

No studies investigating the effect of cold temperature on mental illness morbidity were

identified in the review. Most studies evaluating the effects of ambient temperature on mental

illness used a heat wave approach, only a few used daily temperatures as an exposure variable.

Also the distributed lagged effects of ambient temperature on mental illness-related morbidity

have not been investigated. In addition, none of the studies used a non-linear relationship to

describe the potential association between ambient temperature and mental illness related

morbidity. Finally, only 2 studies investigated the relationship between ambient temperature and

specific mental and behavior disorders.

2.4.2 Ambient Temperature and Mental Illness Related Mortality

24

The studies examining ambient temperature and mental illness/disorder related mortality

are listed in Tables 2-1 and 2-2. Associations between mental illness-related mortality and

periods of elevated temperature were found in Chicago (US), New York (US), Cincinnati (US),

Perth (AUS), Adelaide (AUS), New South Wales (AUS), Brisbane (AUS), and England.

Naughton et al. (2002) (OR 5.7 95%CI1.9-16.8) investigated the effect of heat waves on

mental illness/disorder mortality in Chicago. They found that individuals with mental illness are

significantly more likely to die from extreme heat exposure than individuals without. Similar

results were reported for other US cities by Kaiser et al. (2001) and Semenza et al. (1999).

Nitschke et al. (2007), Hansen et al. (2008), Nitschke et al. (2011) and Wang et al. (2012)

investigated the association between extreme temperature and mental illness mortality in

Brisbane and Adelaide, Australia. Nitschke et al. (2007) reported no increased risk of mental

illness-related mortality for all ages during periods where temperature exceeded 35°C for 3 or

more consecutive days. Hansen et al. (2008) investigated the effects of heat wave on specific

mental illness mortality above heat thresholds of 26.7°C. They reported significant increases in

risk of mortality during heat waves for individuals with a diagnosis of mental illness (ICD 10

F00-F99) and ages 65-74 (IRR: 2.39 95%CI 1.16-4.92) . In addition, they found significant

increases in risk of mortality for dementia (F00-F03), disorders due to psychoactive substance

abuse (F10-F19), and schizophrenia, schizotypal, and delusional disorders (F20-F29). Wang et al.

(2012) also reported an increased risk of mortality for individuals 75+ with mental health during

period of heat waves. In addition, they explored the lagged effects of ambient temperature on

mental illness mortality and reported that the greatest risk occurred with a lag of 1 and the risk

25

decreased thereafter suggesting a possible harvesting effect. However, none of the findings were

statistically significant.

Finally, Page et al. (2012) examined the effect of ambient on mortality among individuals

with psychosis, dementia, and substance misuse. They reported that each 1°C increase above a

heat threshold of 18°C is associated with significant increased risk of mortality for patients with

psychosis, dementia, and substance abuse. In addition, risk did not increase with age; patients

under the age of 65 had a greater risk than patients over 65.

No study has investigated the effect of cold temperature on mental illness related

mortality and ambient temperature. Although mortality risk has been investigated in specific

mental illnesses, the number of studies is relatively small and contains some of the

methodological issues described above. Furthermore, only one study incorporated adjustments

for potential air pollutant confounders or effect modifiers such as, CO2, CO, PM2.5, and NO2.

Although one study incorporated a limited evaluation of the distributed lagged effect of

ambient temperature on mental illness mortality, the lag period was extremely short (2 lags). It

would be relevant to evaluate the effect for much wider range of lags. In addition, all of the

studies assumed a linear relationship between ambient temperature metrics and mortality. Current

studies have indicated the relationship between ambient temperature and adverse health outcomes

to be S, J, or V shaped; thus, prompting the use of nonlinear methods such as spline functions to

model this relationship (Ye et al. 2011). A methodology which allows for the adjustment of

lagged and non-linear effects is the recently developed distributed non-linear modeling (DLNM)

(Gasparrini et al. 2011).

26

2.5 Discussion

This review presents the main findings on the current relationship between temperature

and mental illness related mortality and morbidity. The vulnerability of individuals with mental

and behavior disorders to extreme temperature can be potentially explained by the following

reasons:

(1) Psychiatric/Mental Illness: The pathophysiology of certain mental and behavior

disorders can contribute to dysfunction of normal thermo-regulatory mechanism through

interference of neurotransmitters involved in thermoregulatory pathways (i.e., schizophrenia

spectrum disorder and delusional disorders)(Sung et al. 2011, Kapur et al. 1996, Hermesh et al.

2000). Also, certain pre-existing genetic factors for the development of specific mental and

behavior disorders can induce thermoregulatory dysfunction resulting in hypothermia or

hyperthermia after exposure to extreme temperature (Hasawaga et al. 2005). What occurs from

here onwards is a bit unclear; there is literature to indicate that these changes can exacerbate the

frequency and severity of the symptoms of the individuals while others indicate that these

changes increase the individual’s susceptibility to heat related illness such as a heat stroke. Thus,

they may be admitted to the emergency room or hospital for treatment related to an exacerbation

of their condition triggered by high temperature or heat related illnesses. In more severe cases,

death may result.

(2) Medication/Substance Usage: The use of certain psychotropic medications

(neuroleptics, anxiolytics, anti-depressants, and anti-cholinergic) are known to interfere with

thermoregulation and increase susceptibility to temperature induced morbidity and mortality

(Hansen et al. 2008, Martin-Latry et al. 2007, Hermesh et al. 2000). Also, the use of specific

27

psychoactive substances such as alcohol, hallucinogens (LSD), and stimulants (cocaine,

methamphetamine) can induce hyperthermia increasing the risk of both temperature related

morbidity and mortality (Marzuk et al. 1998, Romanovsky and Blatteis 1998, Crandall et al.

2002).

(3) Behavioral: In many cases, it is possible for individuals with mental and behavior disorders to

be cognitively unaware of their condition and surroundings; thus, disregarding thirst sensations

resulting in possible dehydration (Conti et al. 2007, Kaiser et al. 2001, Green et al. 2001). In

some individuals with mental and behavior disorders, more than one of these factors may be

responsible for the temperature-related morbidity and mortality.

To fully evaluate the studies linking temperature to mental and behavior disorders, several

methodological issues should be considered: 1) exposure assessment, 2) outcome measures, and

3) use of statistical models.

2.5.1 Exposure Assessment

Ten studies used heat waves while others used a combination of mean, minimum, and

maximum temperature for measurement of the exposure. Since heat waves are generally defined

as greater than 35°C (Hansen et al. 2005), they are less informative measures of exposure as the

effect of lower range temperatures and variations in temperature ranges are not well captured;

this may introduce uncertainty in the results. The timing of the heat waves can also have an

impact on effect estimates. For instance, the impact of heat waves on morbidity and mortality

may be stronger earlier in the summer /spring season than heat waves later since the most

vulnerable individuals will be affected by the first heat wave; thus, depleting the pool of

susceptible individuals leaving a smaller pool of susceptible individuals (Basu and Samet 2002).

28

Also, individuals may have acclimatized to heat waves occurring late in summer due to behavior

changes (staying indoors, use of air conditioning, drinking adequate fluids to stay hydrated,

wearing appropriate clothing, etc.) (Goldberg et al. 2011). Thus, the timing of heat waves may

explain some of the heterogeneity among results between heat wave studies.

For studies using temperature (Mean, Min, or Max) as their main exposure metric, several

issues arise as well. For example, most of the studies used an ecological measure of temperature

as a proxy for the entire geographical region of their study. This approach is made on the

assumption that all individuals in the region will experience the same exposure. Thus, some

degree of misclassification is inherent since temperature vulnerabilities do vary with geography

and study temperature may not reflect the actual temperature experienced by the individual at that

particular location (Goldberg et al. 2011). A disadvantage of this approach is that some

regression dilution will likely occur resulting in an underestimation of the true exposure–outcome

relationship (Gasparrini and Armstrong 2010). However, the magnitude of this underestimation

will largely depend on the extent of correlation between the study temperature metric and the

actual temperature experienced by the individual. Also, often one temperature indicator is used

(Mean, or Max, or Min); however, the literature on which temperature metric is most suited for

either morbidity and/or mortality investigations is limited. Thus, it may be more appropriate to

include several temperature metrics and examine the robustness and sensitivity of each indicator

to specific outcomes first before making a selection.

2.5.2 Outcome Measurements

Hospital admissions and emergency room (ER) visits are most often used as measures of

morbidity. Some methodological issues to consider largely pertain to the capture of morbidity

29

data. Usually, hospital admissions and ER visits for mental illness are included in the study. This

approach may not be adequate in capturing individuals admitted for other illnesses triggered from

both temperature and their mental illness. For example, an individual with dementia is exposed to

extreme heat; however, due to the impaired cognitive awareness as a result of the dementia,

he/she did not take the appropriate preventive measures (drinking water, removing excessive

clothing, using air conditioning, staying indoors, etc.) to mitigate thermal stress, becomes

dehydrated, and is admitted to the ER for treatment of dehydration. The ability of the currently

used outcome measurements to capture these individual may not be ideal given that the clinical

manifestations of temperature induced outcomes in patients with mental illness are difficult to

predict and can often encompass a wide range of conditions. Thus, it may be helpful to include

ER visits and hospital admissions where although the primary reason for the visit was not related

to mental illness, the individual had an underlying mental illness condition. However, this

method of outcome measurement for temperature induced morbidity may introduce additional

difficulties in the study design and data collection process.

Some non-acute manifestations of temperature induced health issues experienced by

individual with mental illness may not present to hospital or ER settings at all, but instead present

at family physician offices or social services settings. Inherently, this will induce some level of

underestimation and regression dilution. The exact impact of these issues on the effect estimate of

the study will largely be disease specific. For the measurement of mortality, similar issues will

arise as mental illnesses are not likely to be the immediate cause of death. For instance, an

individual with mental illness susceptible to extreme temperature may die of a cardiovascular

event, dehydration, or heat-stroke induced by exposure to temperature (Semenza et al. 1999).

30

2.5.3 Statistical Methods

Currently, only linear regression models are used to represent the relationship between

ambient temperature and mental illness morbidity and mortality. Although such methods may be

feasible, some limitations are inherent. The assumption of linearity usually requires the analysis

to be partitioned into specific strata either based on month or season (Gasparrini et al. 2011).

Thus, some flexibility to model heterogeneity of the exposure–outcome relationship across an

entire timeframe of the study is lost. Hot and cold temperatures may need to be investigated by

separate models in order to achieve reasonable model fit. Lastly, the ability to account for

delayed/lagged effects of ambient temperature induced outcomes is important given the

“harvesting “nature of these relationships.

2.6 Suggestions for Future Research

The relationship between ambient temperature and mental illness morbidity and mortality

is an important public health issue. To date, numerous studies have demonstrated increases in

both mental illness mortality and morbidity (hospital admissions and emergency room visits)

during periods of elevated temperature. However, given the limited number of studies,

knowledge gaps do exist.

First, no studies have investigated the effects of cold temperature on mental illness

morbidity and mortality. Second, only a few studies have investigated the relationship between

ambient temperature and specific mental diseases such as anxiety disorders, mood disorders,

schizophrenia, and substance abuse related disorders. Third, previous studies have illustrated that

the relationship between ambient temperature and health effects are best described by the J, V, or

U shaped curve and that the impacts of ambient of temperature are often lagged over several

31

days. No study has investigated the distributed lagged effects of temperature on mental illness

related mortality and morbidity.

In addition, the effects of other covariates such as common air pollutants (CO2, NO2, O3,

CO, SO2, PM2.5, PM10) on the relationship between ambient temperature and mental illness

related mortality and morbidity are not well understood. Moreover, most of the studies have used

heat waves as the temperature metric; thus, few studies have investigated these exposure-outcome

relationships using a daily temperature measure (Mean, Min, or Max Temperature) as the

exposure. Also, individual level information such as the use of air conditioning, the use of certain

medications and substances which can increase vulnerability to temperature exposure, and other

lifestyle factors are not accounted for; thus, the extent to which these influence the exposure-

outcome relationship is not well known.

2.7 References

1. Bark, N., 1998. Deaths of psychiatric patients during heat waves. Psychiatr. Serv. 49,

1088-1090.

2. Basu, R. and Samet, J.M., 2002. Relation between elevated ambient temperature and

mortality: a review of the epidemiologic evidence. Epidemiol. Rev. 24, 190-202.

3. Chong, T.W. and Castle, D.J., 2004. Layer upon layer: thermoregulation in schizophrenia



Perini, L., 2005. Epidemiologic study of mortality during the Summer 2003 heat wave in




32


temperature: Advancements and limitations. Environ. Res. 110, 633-638.


Stat. Med. 29, 2224-2234.






10. Hermesh H, Shiloh R, Epstein Y, Manaim H, Weizman A, Munitz H., 2000.Heat


Am J Psychiatry; 157:1327-9.


heat waves on mental health in a temperate Australian city Environ. Health Perspect. 116,

1369-1375.




13. Kaiser, R., Rubin, C.H., Henderson, A.K., Wolfe, M.I., Kieszak, S., Parrott, C.L.,

Adcock, M., 2001. Heat-related death and mental illness during the 1999 Cincinnati heat

wave Am. J. Forensic Med. Pathol. 22, 303-307.



15. Khalaj, B., Lloyd, G., Sheppeard, V., Dear, K., 2010. The health impacts of heat waves in

five regions of New South Wales, Australia: a case-only analysis. Int. Arch. Occup.

Environ. Health. 83, 833-842.




17. Lin, Y., Chang, C., Li, M., Wu, Y., Wang, Y., 2012. High-temperature indices associated

with mortality and outpatient visits: Characterizing the association with elevated

temperature. Sci. Total Environ. 427-428, 41-49.

33


114, 227-231.

19. Martin-Latry, K., Goumy, M.P., Latry, P., Gabinski, C., Begaud, B., Faure, I., Verdoux,

H., 2007. Psychotropic drugs use and risk of heat-related hospitalisation. Eur. Psychiatry.

22, 335-338.

20. Marzuk, P.M., Tardiff, K., Leon, A.C., Hirsch, C.S., Portera, L., Iqbal, M.I., Nock, M.K.,

Hartwell, N., 1998. Ambient temperature and mortality from unintentional cocaine

overdose. JAMA. 279, 1795-1800.









23. Naughton, M.P., Henderson, A., Mirabelli, M.C., Kaiser, R., Wilhelm, J.L., Kieszak,

S.M., Rubin, C.H., McGeehin, M.A., 2002. Heat-related mortality during a 1999 heat

wave in Chicago. Am. J. Prev. Med. 22, 221-227.

24. Nitschke, M., Tucker, G.R., Bi, P., 2007. Morbidity and mortality during heatwaves in

metropolitan. Adelaide Med. J. Aust. 187, 662-665.

25. Nitschke, M., Tucker, G.R., Hansen, A.L., Williams, S., Zhang, Y., Bi, P., 2011. Impact

of two recent extreme heat episodes on morbidity and mortality in Adelaide, South

Australia: a case-series analysis. Environ. Health. 10, 42.

26. Nordon, C., Martin-Latry, K., de Roquefeuil, L., Latry, P., Begaud, B., Falissard, B.,

Rouillon, F., Verdoux, H., 2009. Risk of death related to psychotropic drug use in older

people during the European 2003 heatwave: a population-based case-control study. Am. J.

Geriatr. Psychiatry. 17, 1059-1067.

27. Page, L.A., Hajat, S., Kovats, R.S., Howard, L.M., 2012. Temperature-related deaths in

people with psychosis, dementia and substance misuse. Br. J. Psychiatry. 200, 485-490.

28. Romanovsky, A.A. and Blatteis, C.M., 1998. Pathophysiology of opioids in hyperthermic

states. Prog. Brain Res. 115, 111-127.

34


Hypothermia induced by atypical neuroleptics. Clin. Neuropharmacol. 21, 344-346.

30. Semenza, J.C., Rubin, C.H., Falter, K.H., Selanikio, J.D., Flanders, W.D., Howe, H.L.,

Wilhelm, J.L., 1996. Heat-related deaths during the July 1995 heat wave in Chicago. N.

Engl. J. Med. 335, 84-90.

31. Semenza, J.C., McCullough, J.E., Flanders, W.D., McGeehin, M.A., Lumpkin, J.R., 1999.

Excess hospital admissions during the July 1995 heat wave in Chicago. Am. J. Prev. Med.

16, 269-277.










35. Sung, T., Chen, M. Lin, C. Lung, S., Su, H., 2011. Relationship between mean daily

ambient temperature range and hospital admissions for schizophrenia: Results from a

national cohort of psychiatric inpatients. Sci. Total Environ. 410-411, 41-46.




Environ. Med. 69, 163-169.



heat thresholds and temperature relationships. Sci. Total Environ. 414, 126-33.


impact of summer temperatures and heatwaves on mortality and morbidity in Perth,

Australia. 1994–2008. Environ. Int. 40, 33-38.



28.

35




316, 1210-1218.

36

2.8 Tables

37

Table 2-1 Descriptive Statistics of Included Studies

Author

and

Year of

Publication

Location

and Time

Frame of

Study

Study Objective

of Interest

Study

Design and

Statistical

Analysis

Study Population

and Data Source

Exposure

Variables

Outcomes

Measurements

Variables

Adjusted For

Lags(Days)

Semenza et

al. 1996

Chicago,

United

States

July 12-16,

1995

Determining risk

factors for death

due to heat

Case

Control

Matched

Pair

Analysis

Patients in all 47

non-VA (Veteran

Administration)

hospitals in Cook

County Illinois

during 1995 heat

wave.

Computerized

Inpatient hospital

discharge data for

cook county

obtained from

Illinois Health

Care Cost

Containment

Council

(IHCCCC)

Chicago weather

data for O’Hare

airport obtained

from Midwestern

Climate Center.

Heat Waves Heat Related

Mortality

Age, Race,

Gender, Type of

residence,

Access to air

condition

Social Contacts,

various

comorbidities

None

Bark 1998 New York

City

United

States

1950-1984

Examining

mortality risk

among psychiatric

patients during

periods of heat

waves.

Time Series

Relative

Risk

Calculation

Psychiatric

Patients from ten

state hospitals

within 50 miles of

New York City

from 1950 to

1984.

Heat Wave

Periods

(>89 ° F) for

a period of 3

days or more.

Mortality Gender, Age None

38

Semenza et

al. 1999

Chicago,

United

States

July 1995

Characterization

of the population

at risk for heat

related morbidity.

Time Series Eligible

individuals were

persons older than

24 who died from

July 14 to 17

Data collected by

Vital Statistics

Division of

Department of

Public Health in

Chicago.

Heat Waves Hospital Admissions Age, various

comorbidities

None

Kaiser et al.

2001

Cincinnati,

Ohio,

United

States

July 21st to

August 2nd

1999

Examining the

heat related death

of people with

mental illness.

Case

Control

Study

Matched

Pair

Analysis

All heat related

death reported by

Hamilton County

Coroner Office in

Cincinnati during

heat emergency

from July 21 to

August 2

Heat index

peaks

(41.6°C &

46.1°C)

Median

Ambient

Temperature

Mortality Age, Sex, Race,

Access to air

conditioning,

Outside Activity,

Income, use of

psychotropic

medication, lived

alone

None

Naughton et

al. 2002

Chicago,

United

States

July 29-

August

1999

Determining risk

factors for heat

related death.

Case

Control

Study

Matched

Pair

Analysis

Chicago residents

who died in from

July 26 to August

6, 1999 where heat

was the primarily

reason identified

by Cook County

Medical

Examination’s

Office.

Controls were

matched to

patients by

neighbourhood

and age (within 10

years)

Heat Wave

Periods

Heat related death Age, Gender,

Race, Income,

Living alone,

access to air

conditioning.

Number of

Social Contacts,

Behaviour during

heat wave (Extra

bath/showers)

None

39

Nietschke

et al. 2007

Adelaide,

Australia.

1993-2006

To Investigate the

association

between

morbidity,

mortality and heat

waves in

Metropolitan

Adelaide using

ambulance,

hospital

admissions, and

morality data.

Case Series

Study

Poisson

Regression

Ambulance data

from South

Australian

Ambulance

Services from

1993 to 2006.

Daily cases of

hospital admission

and mortality from

1993 to 2006

Daily Maximum

temperatures were

obtained from

Bureau of

Meteorology Kent

Town Location.

Heave waves

where

maximum

temperature

was

(>35 ° C) for

3 consecutive

days.

Hospital Admissions

(1993-2006)

Mortality

(1993-2004)

Ambulance Transport

(1993-2006)

For Renal, Mental

Illness,

Cardiovascular

Respiratory

Conditions,

Age None

40

Hansen et

al. 2008

Adelaide,

Australia

1993-2005

To identify

mental,

behavioral, and

cognitive

disorders that may

be triggered or

amplified during

heat waves.

Time Series

Poisson

regression

Residents of

Adelaide.

Climatic data were

obtained from

Australian Bureau

of Meteorology

Kent Town

Location.

Morbidity data for

Adelaide are were

obtained from

South Australian

Department of

Health. Principle

hospital discharge

using ISAAC

(Integrated South

Australian Activity

Collection).

Mortality data

were obtained

from July 1993 to

December 2004

from the

Australian Bureau

of Statistics.

Daily

Maximum

Temperature

Hospital Admission

and Mortalities for

MBD ICD(F00F-99)

Organic, including

symptomatic, mental

disorders(F00-F09)

Dementia(F00-F09)

Mental and

Behavioural disorder

due to psychoactive

substance use

(F10-

F19),Schizophrenia

(F20-

F29),Mood(affective

disorders) F30-

F39,Disorders of

Adult personality and

behaviour(F60-F69)

Mental

retardation(F70-F79)

Disorders of

psychological

development(F80-

F89)

(F90-F98) Behavioral

and emotional

disorders

G30-G30.9

Alzheimer’s disease,

G31.1 Senile

degeneration of brain,

R54 senility

Age and Gender None

41

Khalaj et al.

2010

New South

Wales,

Australia

1998-2006

To determine and

characterize

health impacts or

extreme heat

events on

population in five

regions of New

South Wales.

Case Only

Design

Logistic

regression

Residents of New

South West,

Australia.

Daily Hospital

Admission data

from 1998 to 2006

taken from New

South West Health

Climatic Data

from New South

West Bureau of

Meteorology.

Air Pollution data

taken from NSW

department of

Environment,

Climate Change

and Water.

Heat defined

by

Distribution

of Daily

Minimum

Temperature

& Daily

Maximum

Temperature

(Both >99th

Percentile)

Hospital Admissions

(1993-2006)

Mortalities

(1993-2004)

Adjustments of

relevant air

pollution

variables

(NO2, PM10, and

O3)

Using daily mean

levels.

Lag0

Lag1

3 day

moving

average

Nietschke

et al. 2011

Adelaide,

Australia.

1993-2007

Comparison of the

health impacts of

tow

unprecedented

heat waves and

investigating the

association

between health

impacts and

during & intensity

of Adelaide heat

waves.

Case Series

Poisson

Regression

Ambulance call

outs and Hospital

Admission were

taken from July

1993 to March

2009. Mortality

data were

available up until

2007.

Heave waves

where

maximum

temperature

was

(>35 ° C) for

3 consecutive

days

Or more.

Emergency Hospital

Admissions

Age Group None

42

Sung et al.

2011

Taiwan,

Republic

of China

1996-2007

Investigating the

risk of diurnal

temperature range

contributes to the

risk of hospital

admission in

individual with

schizophrenia.

Time Series

GLM

Patient data were

taken from

Psychiatric

Inpatient Medical

Claim from the

National Health

Insurance

Research Database

from 1996 to

2007.

Climatic data were

taken from Central

Weather Bureau of

Taiwan.

Mean Diurnal

Temperature

Hospital Admissions

for

Schizophrenia

(ICD-9 295.0- 295.6

and 295.8-295.9)

Schizoaffective

(ICD-9 295.7)

Age Groups,

Gender,

Geographical

Region

None

43

Williams et

al. 2012 (a)

Perth,

Australia

2003-2009

Examining the

heat thresholds

and temperature

relationships for

mortality and

morbidity

outcomes in

Adelaide.

Time Series

Generalised

Estimating

Equations

(GEEs)

Residents of

Adelaide.

Daily Mortality,

Hospital

admissions, and

emergency

department visits

from July 2003 to

March 2009 were

acquired from

South Australian

Department of

Health.

Ambulance call

outs for the same

period were

obtained from

South Australian

Ambulance

Service.

Climate Data were

taken from the

Australian Bureau

of Meteorology.

Heat

threshold

based on

minimum and

maximum

temperature

Hospital Admission

and Emergency Room

Admissions for

Renal, Mental,

Cardiovascular,

Ischaemic,

respiratory, and

directed heat related

category comprising

dehydration, heat and

sunstroke and

exposure to excessive

heat.

Adjustments

were made for

PM2.5, O3, and

NO2.

None

44

Williams et

al. 2012(b)

Adelaide

South

Australia

1983-2009

Examining the

relationships

between high

temperatures and

adverse health

outcomes for the

population of

Perth and identify

specific health

outcomes that are

sensitive to

extreme heat.

Time Series

Generalized

Estimation

Equations

(GEEs)

Residents of Perth.

Daily all-cause

Mortality (January

1983 – November

2006), hospital

admissions

(January 1980 –

July 2008),

emergency

presentation

(January 2002 –

April 2009).

All data were

obtained from

Government of

Western Australia.

Daily Weather

variables were

taken by the

Australian Bureau

of Meteorology.

Air Pollutant data

were taken from

Department of

Environment and

Conservation.

Maximum

and

Minimum

Daily

Temperature

Daily Mortality,

Hospital Admission ,

and Emergency Room

Admissions for

Renal, Mental,

Cardiovascular,

Ischaemic,

respiratory, and

directed heat related

category comprising

dehydration, heat and

sunstroke and

exposure to excessive

heat.

Age, PM10 and

Ozone

None

45

Wang et al.

2012

Brisbane,

Australia

1996-2005

Assessing the

impact of heat

waves on

mortality and

emergency

hospital admission

and from non-

external causes.

Time Series

Case

Crossover

Residents of

Brisbane. Data

from 1996 to

2005.

Daily Climate data

were taken from

five monitoring

station in Brisbane

from the

Australian Bureau

of Meteorology

Air Pollution data

was provided by

the Queensland

Department of

Environment and

Resource

Management.

Mortality data was

provided by the

Office of

Economic and

Statistical

Research of the

Queensland

Treasury. Daily

data on

Emergency

Hospital

Admissions were

provided by the

Health Information

Centre of

Queensland

Health.

Heat Waves

defined as

daily

maximum

temperature

(≥37°C) for 2

consecutive

days or more.

Mortality and

Emergency Hospital

Admissions for Renal,

Mental, Respiratory,

Ischaemic stroke,

Diabetes, and

Cardiovascular

conditions.

Humidity, PM10,

NO2, O3, Age

Lag1

Lag2

Lag0-2

46

Page et al.

2012

UK

1998-2007

To determine the

risk of mortality

in individuals with

psychosis,

dementia, and

substance misuse

during periods of

hot weather.

Time Series

Poisson

Regression

Individuals with

primary diagnosis

of psychosis,

dementia, and

alcohol misuse &

and other

substance abuse

who had died

between January

1998 and

December 2007.

Daily Temperature

data for 1998-2007

were taken from

the British

Atmospheric Data

Centre. Patient

data were taken

were taken using

the UK General

Practice Research

Database (GPRD).

Daily Mean

Temperature

Heat related mortality

among individuals

with psychosis,

dementia, and

substance misuse.

Geographical

Region, age, use

of antipsychotics,

antidepressants,

hypnotics,

anxiolytics,

None

47

Table 2-2 Effect Estimates of Included Studies

Authors

and

Year of

Publication

Temperature Variable and

Range

Effect Estimate Outcome(Subgroups)

Semenza et

al. 1996

Heat Waves 3.5 (1.7-7.3) Odds ratio during Heat waves for individuals with Mental Problem

Bark 1998 Heat Wave Periods

(>89°F) for a period of 3 days

or more.

1.36

1.36

1.29

1.52

1.74

1.19

Risk of Death for Psychiatric Patient during heat waves from 1950-1984






Semenza et

al . 1999

Heat Waves 20% increase P value =

0.027

50% increase P value =

0.001

Percent Increase in hospital admission for individuals with Nervous system

disorders

Percent Increase in hospital admission for individuals with Degenerative Diseases

Kaiser et

al. 2001

Heat index peaks(41.6°C &

46.1°C)

Median Ambient Temperature

14.0 (1.8-633) Odds ratio for risk of heat related death for individuals with mental illness

Naughton

et al. 2002

Heat Wave Periods 5.7 (1.9-16.8)

4.1 (1.3-12.5)

11.7 (1.5-92.2)

Odds ratio for risk of death during heat waves for individuals with Psychiatric

illness

Odds ratio for risk of death during heat waves for individuals with Depressions

Odds ratio for risk of death during heat waves for individuals with Other Mental

Illness

Nietschke

et al. 2007

Heave waves where maximum

temperature was

(>35 ° C) for 3 consecutive

days.

1.07 (1.01-1.13)

1.52 (0.99-2.32)

1.09 (0.85-1.39)

1.05 (0.99-1.11)

1.12 (1.00-1.26)

1.17 (1.07-1.28)

1.01 (0.73-1.47)

Incidence Rate Ratio(IRR), Hospital Admission during Heat Waves, Mental

Illness(ICD 10 F00-F99)

IRR, hospital admissions during heat waves, all gender, age 0-4, Mental








IRR, hospital admissions during heat waves, all gender, age 75+, Mental


IRR, mortality during heat waves, all gender, all ages, Mental Illness(ICD 10 F00-

48

Authors

and

Year of

Publication


Range


1.24 (0.53-2.91)

2.58 (0.96-6.93)

0.87 (0.64-1.18)

F99)

IRR, mortality during heat waves, all gender, 15-64, Mental Illness(ICD 10 F00-

F99)

IRR, mortality during heat waves, all gender, 65-74, Mental Illness(ICD 10 F00-

F99)

IRR, mortality during heat waves, all gender, 75+, Mental Illness(ICD 10 F00-F99)

Hansen et

al. 2008

Daily Maximum Temperature 1.073 (1.017–1.132)

1.213 (1.091–1.349)

1.174 (1.017-1.355)

1.005 (0.913-1.105)

1.034 (0.969-1.102)

1.091 (1.004-11.85)

1.097 (1.018-1.181)

0.875 (0.678-1.130)

1.049 (0.905-1.214

0.737 (0268-2.026)

1.641 (1.086-2.480)

0.578 (0.349-0.955)

1.154 (0.894-1.489)

7.727 (0.701-85.217)

2.366 (1.200-4.667)

2.395 (1.165-4.922)

5.058 (1.205-21.232)

12.731 (2.064-78.516)

3.098 (1.342-7.155)

3.211 (1.297-7.9848)

IRR, hospital admission associated with heat waves, MBD(ICD F00-F99)

IRR, hospital admission associated with heat waves, Organic, symptomatic, mental

disorders(F00-F09)

IRR, hospital admission associated with heat waves, Dementia (F00-F03)

IRR, hospital admission associated with heat waves, MBD due to substance abuse

(F10-F19)

IRR, hospital admission associated with heat waves, Schizophrenia (F20-F29)

IRR, hospital admission associated with heat waves, Mood Disorders (F30-F39)

IRR, hospital admission associated with heat waves, Neurotic, stress, somatoform

disorders (F40-F48)

IRR, hospital admission associated with heat waves, Behaviour syndrome

associated with physiological factors (F50-F59)

IRR, hospital admission associated with heat waves, Disorders of personality and

behaviour (F60-F69)

IRR, hospital admission associated with heat waves, Mental Retardation (F70-F79)

IRR, hospital admission associated with heat waves, Disorders of psychological

development (F80-F89)

IRR, hospital admission associated with heat waves, Childhood Emotional and

Behaviour disorders (F90-F98)

IRR, hospital admission associated with heat waves, Alzheimer’s disease(G30-

G30.9)

IRR, hospital admission associated with heat waves, Senile degeneration of the

Brain (G31.1)

IRR, hospital admission associated with heat waves, Senility (R54)

IRR, mortality associated with heat waves, age 65-74 years, MBD(F00-F99)

IRR, mortality associated with heat waves, age 15-64, Dementia (F00-F03)

IRR, mortality associated with heat waves, males, 15-64 years, Dementia(F00-F03)

IRR, mortality associated with heat waves, Females, all ages, Disorders due to

substance abuse (F10-F19)

IRR, mortality associated with heat waves, Females, age 15-64 years, Disorders due

49

Authors

and

Year of

Publication


Range


2.079 (1.045-4.138)

2.111 (1.018-4.380)

4.051 (1.386-11.840)

5.255 (1.752-15.758)

to substance abuse (F10-F19)

IRR, mortality associated with heat waves, All ages, Schizophrenia, Schizotypal,

and delusional disorders (F20-F29)

IRR, mortality associated with heat waves, 75+, Schizophrenia, Schizotypal, and

delusional disorders (F20-F29)

IRR, mortality associated with heat waves, male, all ages Schizophrenia,

Schizotypal, and delusional disorders (F20-F29)

IRR, mortality associated with heat waves, male, 75+, Schizophrenia, Schizotypal,

and delusional disorders (F20-F29)

Khalaj et

al. 2010

Heat defined by

Distribution of Daily

Minimum Temperature &

Daily Maximum Temperature

(Both >99th

Percentile)

1.04 (0.99 -1.10)

1.01 (0.96 -1.07)

1.07 (1.00 -1.15)

1.07 (1.03-1.11)

1.05 (1.01-1.09)

1.11 (1.06-1.17)

Relative Odds, emergency hospital admission during heat waves vs. other

condition, MBD(F00-F99), lag 0


condition, MBD(F00-F99), lag 1


condition, MBD(F00-F99), 3 day average

Relative Odds, emergency hospital admission during heat waves vs. no condition ,

MBD(F00-F99), lag 0


MBD(F00-F99), lag 1


MBD(F00-F99), 3 day average

Nietschke

et al. 2011

Heave waves where maximum

temperature was

(>35 ° C) for 3 consecutive

days or more.

1.05 (1.00 -1.10)

1.48 (0.99 -2.21)

1.03 (0.81- 1.32)

1.04 (0.99 -1.09)

1.12 (1.01-1.24)

1.10 (1.01-1.19)

0.98 (0.84-1.14)

1.64 (0.70-3.87)

IRR, daily hospital admission for Mental health during heat wave, All ages, Heat

wave pre 2008

IRR, daily hospital admission for Mental health during heat wave, 0-4, Heat wave

pre 2008


pre 2008

IRR, daily hospital admission for Mental health during heat wave, 15-64, Heat

wave pre 2008


wave pre 2008

IRR, daily hospital admission for Mental health during heat wave, 75+, Heat wave

pre 2008


wave 2008


50

Authors

and

Year of

Publication


Range


0.97 (0.83-1.12)

0.87 (0.62-1.22)

1.10 (0.85-1.41)

1.03 (0.88-1.20 )

0.84 (0.34-2.09 )

1.03 (0.89-1.21 )

1.03 (0.74-1.42 )

1.05 (0.8-1.36))

1.11 (1.04-1.18)

1.98 (0.78-4.99)

0.68 (0.41-1.14)

1.09 (1.02-1.17)

1.59 (1.22-2.08)

1.15 (0.93-1.43)

1.05 (0.96-1.14)

0.56 (0.08-4.18)

0.96 (0.53-1.73)

1.04 (0.95-1.13)

2008


wave 2008


wave 2008


2008


wave 2009


2009


wave 2009


wave 2009


2009

IRR, daily emergency room admissions for Mental health during heat wave, All

ages, Heat wave Pre 2008

IRR, daily emergency room admissions for Mental health during heat wave, 0-4,

Heat wave Pre 2008


Heat wave Pre 2008


Heat wave Pre 2008


Heat wave Pre 2008

IRR, daily emergency room admissions for Mental health during heat wave, 75+,

Heat wave Pre 2008


ages, Heat wave 2008


Heat wave 2008


Heat wave 2008


51

Authors

and

Year of

Publication


Range


1.24 (0.85-1.82)

1.15 (0.78 1.51)

1.04 (0.95-1.13)

0.62 (0.83-4.60)

0.96 (0.58-1.59)

1.05 (0.96-1.10)

0.66 (0.40-1.09)

1.18 (0.85-1.64)

Heat wave 2008


Heat wave 2008

IRR, daily emergency room admissions for Mental health during heat wave, 75,

Heat wave 2008


ages, Heat wave 2009


Heat wave 2009


Heat wave 2009


Heat wave 2009


Heat wave 2009

IRR, daily emergency room admissions for Mental health during heat wave, 75,

Heat wave 2009

Sung et al.

2011

Mean Diurnal Temperature 1.22 (1.08-1.37)

1.16 (1.05-1.28)

1.17 (1.07-1.29)

1.13 (1.03-1.23)

1.13 (1.04-1.24)

1.15 (1.05-1.26)

1.10 (1.01-1.20)

1.02 (0.93-1.12)

1.01 (0.92-1.11)

Mean daily range of temperature and Relative risk of Schizophrenia Admissions,

99th

percentile of range


95-98th

percentile of range


90-94th

percentile of range


75-89th

percentile of range


50-74th

percentile of range


25-49th

percentile of range


10-24th

percentile of range

Mean daily range of temperature and Relative risk of Schizophrenia Admissions, 5-

9th

percentile of range

Mean daily range of temperature and Relative risk of Schizophrenia Admissions, 1st

percentile of range

52

Authors

and

Year of

Publication


Range


Williams et

al. 2012(a)

Heat threshold based on

minimum and maximum

temperature

1.027 (0.987-1.068)

1.051 (1.027-1.074)

1.030 (0.989-1.071)

IRR for daily Mental Health Emergency Room Visits per 10°C increase in Max

Temp, Adjusted for O3, NO2, PM2.5

IRR for daily Mental Health Emergency Room Visits per 10°C increase in Min

Temp, Adjusted for O3, NO2, PM2.5

IRR for daily Mental Health Emergency Room Visits during heat waves, Adjusted

for O3, NO2, PM2.5

Williams et

al. 2012(b)

Maximum and Minimum

Daily Temperature

1.154 (0.970-1.373)

1.079 (1.027-1.133)

1.143 (1.016-1.285)

1.001 (0.993-1.010)

1.006 (0.978-1.034)

IRR for daily Mental Health Emergency Room Visits per 10°C increase in Max

Temp, Adjusted for O3, PM10, Age 65+


Temp, Adjusted for O3, PM10, All ages


Temp, Adjusted for O3, PM10, Age 65+

IRR for daily Mental Health Emergency Room Visits per 1°C increase during

elevated periods, Adjusted for O3, PM10, All ages

IRR for daily Mental Health Emergency Room Visits per 1°C increase during

extreme periods, Adjusted for O3, PM10, All ages

Wang et al.

2012

Heat Waves defined as daily

maximum temperature

(≥37°C) for 2 consecutive days

or more.

0.88 (0.71-1.11)

0.88 (0.70-1.10)

0.90 (0.72-1.12)

0.88 (0.71-1.11)

1.50 (0.45-4.99)

1.53 (0.46-5.08)

1.52 (0.46-5.05)

1.49 (0.45-4.98)

0.60 (0.20-1.75)

0.59 (0.20-1.74)

OR of Mental Health Emergency Hospital Admissions during Heat waves, Aged

15-64, Adjusted for O3


15-64, Adjusted for PM10


15-64, Adjusted for NO2


15-64, Adjusted for O3, PM10, NO2


65-74, Adjusted for O3


65-74, Adjusted for PM10


65-74, Adjusted for NO2


65-74, Adjusted for O3, PM10, NO2


75+, Adjusted for O3


75+, Adjusted for PM10

53

Authors

and

Year of

Publication


Range


0.60 (0.20-1.75)

0.59 (0.20-1.73)

0.87 (0.70-1.08)

0.86 (0.70-1.07)

0.88 (0.71-1.09)

0.86 (0.70-1.07)

1.05 (0.27-4.10)

1.33 (0.34-5.16)

1.21 (0.31-4.72)

1.08 (0.27-4.23)

0.80 (0.21-2.98)

1.04 (0.28-3.86)

0.92 (0.25-3.42)

0.82 (0.22-3.06)

1.16 (0.30-4.40)

1.13 (0.30-4.28)

0.92 (0.24-3.42)

0.85 (0.69-1.06)

0.86 (0.70-1.07)

0.87 (0.70-1.08)


75+, Adjusted for NO2


75+, Adjusted for O3, PM10, NO2

OR of Mental Health Emergency Hospital Admissions during Heat waves, Total,

Adjusted for O3


Adjusted for PM10


Adjusted for NO2


Adjusted for O3, PM10, NO2

OR of Mental Health Mortality during Heat waves, Age 75+, Adjusted for O3

OR of Mental Health Mortality during Heat waves, Age 75+, Adjusted for PM10

OR of Mental Health Mortality during Heat waves, Age 75+, Adjusted for NO2

OR of Mental Health Mortality during Heat waves, Age 75+, Adjusted for O3,

PM10, NO2

OR of Mental Health Mortality during Heat waves, All ages Adjusted for O3

OR of Mental Health Mortality during Heat waves, All ages Adjusted for PM10

OR of Mental Health Mortality during Heat waves, All ages, Adjusted for NO2

OR of Mental Health Mortality during Heat waves, All ages Adjusted for O3, PM10,

NO2

OR of Mental Health Mortality during Heat waves, Adjusted for O2,PM10,NO2,

Lag 1


Lag 2


Lag 0-2

OR of Mental Health Related Emergency Visits, Adjusted for O2,PM10,NO2,

Lag 1

OR of Mental Health Related Emergency Visits, Adjusted for O2,PM10,NO2,

Lag 2

OR of Mental Health Related Emergency Visits, Adjusted for O2,PM10,NO2, Lag 0-

2

54

Authors

and

Year of

Publication


Range


Page et al.

2012

Daily Mean

Temperature

1.10 (1.05-1.16)

1.01 (1.01-1.07)

1.03 (1.00-1.07)

1.02 (0.95-1.09)

1.08 (1.04-1.13)

1.20 (1.08-1.35)

1.04 (1.00-1.08)

1.07 (1.01-1.13)

1.02 (0.96-1.09)

1.08 (1.02-1.15)

1.11 (1.02-1.20)

RR of Mortality and Temperature for Psychiatric Patients Per 1°C increase in Temp

above 93rd

Percentile , Age<65

RR of Mortality and Temperature for Psychiatric Patients Per 1°C increase in Temp

above 93rd

Percentile , Age65+

RR of Mortality and Temperature for Dementia Per 1°C increase in Temp above

93rd

Percentile

RR of Mortality and Temperature for Psychoses Per 1°C increase in Temp above

93rd

Percentile

RR of Mortality and Temperature for Alcohol Misuse Per 1°C increase in Temp

above 93rd

Percentile

RR of Mortality and Temperature for Other substance abuse Per 1°C increase in

Temp above 93rd

Percentile

RR of Mortality and Temperature for All Psychiatric Patients Per 1°C increase in

Temp above 93rd

Percentile

RR of Mortality and Temperature for Antipsychotics Per 1°C increase in Temp

above 93rd

Percentile

RR of Mortality and Temperature for Anti-depressants Per 1°C increase in Temp

above 93rd

Percentile

RR of Mortality and Temperature for Hypnotics/Anxiolytics Per 1°C increase in

Temp above 93rd

Percentile

RR of Mortality and Temperature for Hypnotics/Anxiolytics excluding alcohol or

drug misuse Per 1°C increase in Temp above 93rd

Percentile

55

Chapter 3

Acute Impacts of Extreme Temperature Exposure on Emergency Room Visits

Related to Mental and Behavior Disorders in the City of Toronto, Canada.

56

3.1 Abstract

Objectives: The purpose of this study was to assess the effects of extreme ambient temperature on

hospital emergency room (ER) visits related to mental and behavior disorders in Toronto, Canada.

Methods: A time series study was conducted using health and climatic data from April 1st 2002 to March

31st 2010. Relative risks for increases in ER visits were estimated for specific mental and behavior

disorders (MBD) after exposure to hot and cold temperatures while using 50th percentile of the mean

temperature distribution as the reference. The non-linear nature of the exposure–outcome relationship was

accounted for using a distributed lag non-linear model (DLNM). The effects of seasonality, humidity, day

of the week and outdoor air pollutants (CO2, O3, PM2.5, NO2, and SO2) were also adjusted.

Results: We observed positive associations between elevated mean temperatures and hospital ER visits

for MBD. For hot temperatures, significant increases in ER visits for MBD were observed after a mean

temperature threshold of about 24°C. The association generally lasted about 3 to 4 lag days with the

strongest effect occuring at lag 0 (RR = 1.06; 95% CI: 1.03 - 1.09). Similar trends and associations were

observed for specific mental illnesses such as mood, neurotic, substance abuse, and schizophrenia related

disorders. Cold temperature associations were only observed for schizophrenia.


individuals with mental and behavior disorders. Patient management and education may need to be

improved as extreme temperatures become more prevalent.

57

3.2 Introduction

Ambient temperature has long been suspected to play a role in the psychotic exacerbation

of core symptoms for many specific mental and behavior disorders (McMichael et al. 2006).

Individuals with mental illness are often susceptible to the effects of extreme temperature due the

disruption of normal thermoregulation from psychotropic medication usage, psychiatric illness,

and various behavior symptoms (Hansen et al. 2008; Shiloh et al. 2005). Several studies have

demonstrated increases in hospital admissions and emergency rooms (ER) visits during periods

of elevated temperature for individuals with mental illness in Australia (Nitschke et al. 2007,

Nitschke et al. 2011, Williams et al. 2012(a), Williams et al. 2012(b), Wang et al. 2012, Khalaj et

al. 2010), United States (Semenza et al. 1999, Semenza et al. 1996, Kaiser et al. 2001), Taiwan

(Sung et al. 2011), England and Wales (Page et al. 2012). However, the distribution of these

increases in morbidity with respect to specific mental illnesses (mood disorders, schizophrenia,

substance abuse, etc.) is not well known. In addition, given that the effect of ambient temperature

on human health is distributed over a period of several days to over one week (Ye et al. 2011),

the lagged structure of the effects of ambient temperature on mental and behavior disorders

remains unknown.

The aim of our study was to investigate the relationship between ambient temperature and

mental illness related emergency room admissions in the City of Toronto. Toronto is located in

the southern part of the province of Ontario; it is the largest city in Canada and the provincial

capital of Ontario with approximately 2.7 million residents (Statistics Canada 2011). The city

experiences a wide range of hot and cold temperatures throughout the year with daily mean

temperatures ranging from -20.3 to 31.3°C (Environment Canada). How extreme temperature

(Hot and Cold) can exacerbate specific mental illnesses in Toronto and the lagged effects of such

associations are of interest.

58

3.3 Methods

3.3.1 Study Population and Morbidity Data

The study population included the residents of Toronto from April 1st 2002 to March 31

st

2010. The study population was identified using relevant postal codes from a provincial

computerized database for tracking emergency room admissions: the National Ambulatory Care

Reporting System (NACRS). Access to the morbidity data was granted through data sharing

agreement between the Canadian Institute of Health Information (CIHI) and the Public Health

Agency of Canada (PHAC). The study protocol was reviewed and approved by the Queen’s

University Health Sciences & Affiliated Teaching Hospitals Research Ethics Board (see

Appendix A).

Daily emergency room (ER) visits was selected as the outcome metric to represent the

acute effect of ambient temperature on mental illness morbidity. This was done after reviewing

the current literature between mental illness related morbidity and exposure to ambient

temperature. Daily ER visits were obtained from NACRS for the city of Toronto from April 1st

2002 to March 31st 2010. ER records were retrieved if the primary reason of the visit was

classified as related to mental and behavior disorders (F00-F99; under the International

Classification of Diseases 10th

Revision). We also investigated ambient temperature effects for

specific diseases: Schizophrenia (F20-F29), Mood Disorders (F30-F39), Neurotic Disorders

(F40-F48), and Mental and Behavior Disorders due to Psychoactive Substance (F10-F19).

3.3.2 Weather and Air Pollution Data

Hourly measurements of temperature and humidity for the city of Toronto from April 1st

2002 to March 31st 2010 were taken from Environment Canada using the monitoring station at

59

Toronto Pearson International Airport (latitude:43°40’36”N; longitude: 79°37’50”W) located

approximately 26 km west of downtown Toronto. Daily mean, minimum and maximum

temperatures and mean humidity were computed. Air pollution data were obtained from the

National Air Pollution System (NAPS). It is comprised of hourly measurements of gaseous

pollutants: sulfur dioxide (SO2), nitrogen dioxide (NO2), carbon monoxide (CO), and ozone (O3).

The air pollutants were measured using UV Fluorescence (SO2) chemiluminescence (NO2),

infrared gas correlation (CO), and UV absorption (O3) from multiple (Max8, Min4) fixed weather

monitoring station within Toronto. Mean daily concentrations of each pollutant were computed

by taking daily averages and then averaging across multiple stations for each day. The daily mean

of particulate matter (PM2.5) was computed using the same method and was measured using a

combination of tapered element oscillating microbalances, gravimetric filters, virtual impactors,

and beta radiation attenuation (Environment Canada).

3.3.3 Time Series Dataset

The time series dataset was based on the temporal structure of the data, where both the

exposure (daily mean temperature), the outcome (daily counts of hospital emergency room visits

for mental illnesses), and the confounders (daily concentration of pollutants and days of the

week) were ordered within equally spaced time intervals of one day. A visual example of the

dataset is provided in Appendix B along with two graphs to demonstrate the structure of the data.

3.3.4 Statistical Methods

Daily mean temperature was used as the exposure metric after model selection with

several other indices of temperature and we assessed its association with emergency room visits

60

related to mental and behavior disorders using a time series study design. Given that the

relationship between adverse health outcomes and ambient temperature are lagged across specific

timeframes and best described using a non-linear curve (J, S, or V) (Ye et al. 2011, Turner et al.

2012, Basu and Samet 2002), we employed the use of distributed lag non-linear models (DLNM).

The DLNM is built on the foundation of combining two functions (temperature and lag) into a

“cross-basis”, a bi-dimensional matrix which allows the estimation of possible non-linear

temperature and morbidity effects across specific lag periods (Gasparrini and Armstrong 2010;

Gasparrini et al. 2011). In the context of our study, the DLNM was used to estimate the relative

risk of increase ER visits from exposure to ambient temperature on mental and behavior disorders

and specific diseases: schizophrenia, substance abuse related mental and behavior disorders,

mood disorders, and neurotic disorders for both hot and cold temperatures.

For extreme hot temperatures, the 99th

percentile of the daily mean temperature was used

as the exposure metric and the 1st percentile of the daily mean temperature was used to represent

exposure to extreme cold temperatures. We used the 50th

percentile of the temperature as a

reference. A quasi Poisson regression was fitted using generalized linear modeling (GLM) to

model the daily counts of emergency room visits based on the predictor variables: daily mean

temperature and lag. The degrees of freedom for mean temperature and lag were set to 5. The lag

period for the DLNM was set to 30 days. To account for the effects of season and sub-seasonal

cycles, a categorical variable for day of the week was included. In addition, a smoothing function

for time of 10 degrees of freedom per annum was included and we also investigated the

sensitivity of the results by using varying degrees of freedom: 6, 7, and 8. An equation of the

model is given in Appendix C. The quasi Akaike’s information criterion (QAIC) was used to

61

measure the relative goodness of fit for our statistical models. A lower QAIC value indicates an

improvement in model fit.

3.3.5 Potential Confounders

We adjusted for the effects of air pollution by including mean daily concentration of NO2,

CO and O3 as covariates in our analysis. Based on previous work, the effects of these pollutants

were adjusted for lag periods of 0-2 days (Goldberg et al. 2011, Ye et al. 2011, Gosling et al.

2008). In addition, we accounted for humidity by using the daily mean concentration as a

covariate. Finally, we adjusted for the effects of age by examining the association between

ambient temperature and mental illness related ERV in specific age groups (0-14, 15-39, 40-59,

60+). Gender was adjusted using the same approach.

3.4 Results

3.4.1 Ambient Temperature and Environment Characteristics

Table 3-1 illustrates the descriptive statistics for weather and air pollution variables

during the study period from April 1st 2002 to March 31

st 2010. The average mean daily

temperature was 8.7°C with range from -20.3°C to 31.5°C (Interquartile range 17.9°C). The

mean relative humidity for the study period was 69.30% ranging from minus25.57 to 98.57%. Air

pollution in the City of Toronto was low compared with other major Canadian cities. The average

mean daily concentrations for collected air pollutants were: O3 (22.05), CO (0.355Ug/m3), SO2

(2.226Ug/m3), NO2 (20.47Ug/m3, and PM2.5 (8.39ppb). Table 3-2 indicates that temperature

metrics (Mean, Min, and Max) were highly correlated with each other (Pearson Correlation

Coefficients > 0.95), all negatively correlated with air pollutants NO2, CO, and SO2, and all

positively correlated with O3 and PM2.5. Collinearity was not a concern since none of the air

pollutants were significantly correlated with each other and with the temperature metrics.

62

Table 3-1Descriptive Statistics

Units Number of Days

of Measurement

Mean Standard

Deviation

Maximum Minimum 25th

50th 75

th 100th Interquartile

Range

Maximum

Temperature

° C 2922 13.33 11.46 36.6 -17.4 3.7 13.7 23.4 36.6 19.7

Minimum

Temperature

° C 2922 4.058 10.21 26.3 -24.7 -3.1 4.2 12.9 26.3 16

Mean

Temperature

° C 2922 8.706 10.72 31.5 -20.3 0.3 9.0 18.2 31.5 17.9

Relative

Humidity

% 2922 69.30 14.37 98.75 -25.57 61.87 70.54 78.32 98.75 16.45

PM2.5 ppb 2922 8.39 6.85 49.2 0 3.833 6.33 10.5 49.2 6.66

NO2 Ug/m3 2922 20.47 7.71 62.60 5 15.0 19.4 24.8 62.6 9.8

O3 Ug/m3 2920 22.05 12.26 248.00 2.20 14.6 21 28 248.0 13.4

CO Ug/m3 2915 0.355 0.210 1.8 0 0.2 0.3 0.45 1.8 0.25

SO2 Ug/m3 2922 2.226 2.1158 17.33 0 1 1.5 3 17.33 2

63

Table 3-2 Correlation Coefficients

MINT MEANT MAXT AVGSO2 AVGPM2.5 AVGSO2 AVGNO2 AVGO3 AVGCO

MINT 1 0.98626 0.95113 -0.11074 0.388 -0.11074 -0.24325 0.30521 -0.12671

MEANT 0.98626 1 0.98907 -0.07349 0.41595 -0.07349 -0.19674 0.33684 -0.11246

MAXT 0.95113 0.98907 1 -0.03843 0.43179 -0.03843 -0.15045 0.35778 -0.09724

AVGSO2 -0.11074 -0.07349 -0.03843 1 0.41422 1 0.58108 -0.0779 0.55707

AVGPM2.5 0.388 0.41595 0.43179 0.41422 1 0.41422 0.44632 0.25921 0.28585

AVGSO2 -0.11074 -0.07349 -0.03843 1 0.41422 1 0.58108 -0.0779 0.55707

AVGNO2 -0.24325 -0.19674 -0.15045 0.58108 0.44632 0.58108 1 -0.19843 0.54676

AVGO3 0.30521 0.33684 0.35778 -0.0779 0.25921 -0.0779 -0.19843 1 -0.0945

AVGCO -0.12671 -0.11246 -0.09724 0.55707 0.28585 0.55707 0.54676 -0.0945 1

MINT=Daily Minimum Temperature

MEANT= Mean Daily Temperature

MAXT= Daily Max Temperature

AVGSO2= Daily Mean SO2 Concentration

AVGPM2.5= Daily Mean PM2.5 Concentration

AVGNO2 = Daily Mean NO2 Concentration

AVGO3 = Daily Mean Ozone Concentration

AVGCO = Daily Mean Carbon Monoxide Concentration

64

3.4.2 Distributed Effects of Ambient Temperature on Emergency Room Visits

Table 3-3 summaries the association between daily mean temperatures and mental and

behavior disorders estimated through the use of DLNM. Results are shown for heat (99th

percentile of mean temperature) and cold (1st percentile of mean temperature) effects.

Adjustments were made for, humidity, NO2, CO and O3. Cumulative effects were calculated by

aggregating daily risks over a 7-day period. Sensitivity analyses were done with different lag

periods to assess the robustness of the results. The regression results indicated that mental and

behavior disorders (ICD-10 F00-F99), schizophrenia (F20-F29), mood disorders (F30-F39),

neurotic disorders (F40-F48), and MBD due to substance abuse (F10-19) were all significantly

associated with high temperature (99th

percentile) in the City of Toronto while schizophrenia

(F20-F29) and neurotic (F40-F48) disorders related ER visits showed some associations with

extreme cold temperature (1st percentile). The specific associations are described in the

subsequent sections.

65

Table 3-3 Distributed Lag Effects of Ambient Temperature on various Mental Illnesses

The Model was fitted using cubic b-spline using three equally space knots and 4 degree of freedom for mean temperature, a natural cubic spline

with 5 df for the lag space, a natural cubic spline with 10 df per year was applied, adjustments were included for NO2,CO,O3, Days of the week,

and Humidity.

Effects of 99th percentile of Mean Ambient Temperature on Emergency Room Admissions (ERV) across all lags(0-30)

Mental Behavior

Disorders (MBD)

(F00-F99)

MBD due to

Psychoactive Substance

(F10-F19)

Schizophrenia

(F20-29)

Mood Disorders

(F30-39)

Neurotic Disorders

(F40-F48)

Lag

Days

Relative Risk

(95% CI)

Relative Risk

(95% CI)

Relative Risk

(95% CI)

Relative Risk

(95% CI)

Relative Risk

(95% CI)

Lag0 1.056 (1.026 -1.087) 1.046 (0.986 - 1.110) 1.097 (1.026 - 1.172) 1.059 (0.996 - 1.126) 1.023 (0.943 - 1.108)

Lag1 1.040 (1.021-1.060) 1.028 (0.990 - 1.067) 1.078 (1.033 - 1.125) 1.050 (1.010 - 1.091) 1.031 (0.994 - 1.069)

Lag2 1.027 (1.014-1.400) 1.013 (0.978 - 1.040) 1.061 (1.030 - 1.094) 1.041 (1.013 - 1.071) 1.027 (0.987 - 1.068)

Lag3 1.017 (1.005-1.030) 1.003 (0.970 - 1.029) 1.047 (1.017 - 1.079) 1.034 (1.006 - 1.062) 1.014 (0.982 - 1.046)

Lag4 1.011 (0.998-1.024) 0.997 (0.970 - 1.235) 1.036 (1.005 - 1.068) 1.027 (0.998 - 1.056) 1.002 (0.977 - 1.028)

Lag5 1.006 (0.993-1.019) 0.993 (0.968 - 1.019) 1.026 (0.996 - 1.058) 1.021 (0.993 - 1.049) 0.994 (0.969 - 1.021)

Lag6 1.030 (0.991-1.015) 0.992 (0.968 - 1.017) 1.018 (0.990 - 1.048) 1.016 (0.989 - 1.042) 0.991 (0.964 - 1.019)

Lag7 1.001 (0.990-1.013) 0.991 (0.969 - 11.02) 1.011 (0.985 - 1.039) 1.011 (0.986 - 1.036) 0.989 (0.961 - 1.018)

Lag8 0.999 (0.989-1.010) 0.991 (0.969 - 1.014) 1.005 (0.980 - 1.032) 1.007 (0.983 - 1.030) 0.990 (0.965 - 1.017)

Lag9 0.998 (0.987-1.009) 0.991 (0.969 - 1.014) 1.000 (0.975 - 1.027) 1.003 (0.980 - 1.027) 0.992 (0.968 - 1.018)

Lag10 0.997 (0.986-1.008) 0.992 (0.969 - 1.014) 0.997 (0.971 - 1.023) 1.002 (0.977 - 1.024) 0.995 (0.972 - 1.018)

Lag11 0.997 (0.985-1.008) 0.992 (0.969 - 1.015) 0.993 (0.968 - 1.020) 0.998 (0.974 - 1.022) 0.998 (0.975 - 1.020)

Lag12 0.996 (0.984-1.007) 0.993 (0.970 - 1.016) 0.991 (0.964 - 1.018) 0.996 (0.972 - 1.207) 0.999 (0.977 - 1.023)

Lag13 0.996 (0.984-1.008) 0.993 (0.970 - 1.017) 0.989 (0.962 - 1.017) 0.994 (0.970 - 1.019) 1.001 (0.979 - 1.025)

Lag14 0.996 (0.984-1.008) 0.994 (0.971 - 1.018) 0.988 (0.962 - 1.016) 0.993 (0.968 - 1.019) 1.003 (0.980 - 1.027)

Lag15 0.996 (0.984-1.008) 0.995 (0.972 - 1.019) 0.988 (0.961 -1.016) 0.993 (0.968 - 1.018) 1.005 (0.982 - 1.029)

Lag16 0.997 (0.985-1.008) 0.997 (0.973 - 1.020) 0.988 (0.961 - 1.016) 0.992 (0.968 - 1.017) 1.006 (0.983 - 1.030)

Lag17 0.998 (0.986-1.009) 0.998 (0.974 - 1.020) 0.989 (0.962 - 1.016) 0.992 (0.968 - 1.017) 1.008 (0.985 - 1.031)

Lag18 0.999 (0.987-1.010) 0.999 (0.977 - 1.022) 0.989 (0.963 - 1.017) 0.993 (0.968 - 1.017) 1.009 (0.986 - 1.032)

Lag19 1.000 (0.988-1.010) 0.999 (0.977 - 1.023) 0.990 (0.966 - 1.018) 0.993 (0.970 - 1.017) 1.010 (0.988 - 1.032)

Lag20 1.000 (0.988-1.011) 1.000 (0.978 - 1.023) 0.994 (0.969 - 1.019) 0.994 (0.971 - 1.017) 1.010 (0.989 - 1.033)

Lag21 1.001 (0.989-1.012) 1.002 (0.983 - 1.024) 0.996 (0.971 - 1.021) 0.995 (0.973 - 1.018) 1.011 (0.990 - 1.033)

Lag22 1.000 (0.990-1.013) 1.004 (0.985 - 1.025) 0.999 (0.975 - 1.024) 0.997 (0.975 - 1.019) 1.012 (0.991 - 1.033)

66

Effects of 99th percentile of Mean Ambient Temperature on Emergency Room Admissions (ERV) across all lags(0-30)

Mental Behavior

Disorders (MBD)

(F00-F99)

MBD due to

Psychoactive Substance

(F10-F19)

Schizophrenia

(F20-29)

Mood Disorders

(F30-39)

Neurotic Disorders

(F40-F48)

Lag

Days

Relative Risk

(95% CI)

Relative Risk

(95% CI)

Relative Risk

(95% CI)

Relative Risk

(95% CI)

Relative Risk

(95% CI)

Lag23 1.000 (0.992-1.015) 1.006 (0.987 - 1.027) 1.000 (0.978 - 1.027) 0.998 (0.977 - 1.020) 1.012 (0.992 - 1.033)

Lag24 1.006 (0.994-1.016) 1.008 (0.989 - 1.029) 1.006 (0.982 - 1.030) 1.000 (0.978 - 1.022) 1.013 (0.993 - 1.033)

Lag25 1.008 (0.996-1.019) 1.011 (0.990 - 1.030) 1.009 (0.985 - 1.034) 1.002 (0.980 - 1.025) 1.013 (0.992 - 1.034)

Lag26 1.010 (0.997-1.020) 1.013 (0.992 - 1.033) 1.013 (0.988 - 1.040) 1.004 (0.981 - 1.028) 1.013 (0.992 - 1.035)

Lag27 1.010 (1.000-1.023) 1.015 (0.993 - 1.039) 1.017 (0.990 - 1.045) 1.060 (0.982 - 1.031) 1.014 (0.991 - 1.037)

Lag28 1.013 (1.001-1.026) 1.017 (0.993 - 1.043) 1.021 (0.993 - 1.051) 1.009 (0.982 - 1.035) 1.014 (0.991 - 1.039)

Lag29 1.016 (1.001-1.029) 1.020 (0.994 - 1.047) 1.026 (0.995 - 1.058) 1.019 (0.983 - 1.039) 1.014 (0.989 - 1.041)

Lag30 1.017 (1.002-1.032) 1.020 (0.994 - 1.051) 1.030 (0.997 - 1.065) 1.013 (0.983 - 1.044) 1.014 (0.987 - 1.044)

67

3.4.3 Mental and Behavior Disorders (MBDs) (ICD-10 F00-F99)

A total of 271,746 emergency room visits were related to mental and behavior disorders

in Toronto during the study time frame. We observed a significant increase (RR1.06 95%CI 1.03-

1.09) in ER visits at the 99th percentile of the mean temperature at lag 0. The significant increase

in ER visits lasted for 3 days (RR1.04 95%CI 1.02-1.06 lag 1, RR1.03 95%CI 1.01-1.04 lag 2,

RR1.02 95%CI 1.01-1.03 at lag 3) gradually decreasing and eventually falling back around RR of

1.00 (Table 3-3). Although, there was an increase in risk at lag 28-30 for high temperatures

(Table 3-3), this should be interpreted with caution as the stability of estimates generated by

DLNM at extremely long lags are more prone to variability. Cumulatively, a 29% increase

(RR1.29 95%CI 1.09-1.53) in daily ER visits was observed over one week after the initial high

temperature day (Figure 3-1).

68

Figure 3-1 Cumulative Effect of Ambient Temperature on Mental and Behavior Illnesses-Related

ER Visits (The grey portion of the figures indicates confidence intervals and the red line represents the relative risk

estimates. The figure was produced from model using 4 degrees of freedom for lag and 5 degrees of freedom for

temperature. Adjustments were made for NO2, CO, O3, day of the week and daily humidity levels. Seasonality

smoothing was set to 10 per annum.)

No increase risk in admissions was observed for cold temperatures. Age and gender

effects were also examined. The effect of high temperature on MBDs related ER visits was

greatest among individuals aged 60+ (Figure 3-2). There were no gender effects.

69

Figure 3-2 Lagged Effects of Mean Ambient Temp on MBD based on age (The grey portion of the

figures indicates confidence intervals and the red line represents the relative risk estimates. The figure was produced

from model using 4 degrees of freedom for lag and 5 degrees of freedom for temperature. Adjustments were made

for NO2, CO, O3, day of the week and daily humidity levels. Seasonality smoothing was set to 10 per annum.)

3.4.4 MBDs due to psychoactive substance abuse (ICD-10 F10-F19)

In this category, we observed 73,050 emergency room visits related to MBDs due to

psychoactive substance abuse over the study timeframe in Toronto. Increased ER visits at high

(99th

percentile) mean temperatures were observed from 0 to 3 lag days (Table 3-3). The highest

increase in ER visits was observed at lag 0 (RR 1.05 95%CI 0.99-1.11). After a period of 3 days

the effect of ambient temperature on substance abuse ER visits fell to null levels. Finally, a 14%

increase (7 day cumulative) (RR 1.14 95%CI 1.03-1.32) in daily emergency room visits was

70

observed after a high temperature day (Figure 3-3). No increases in ER visits for cold

temperatures were observed.

Figure 3-3 Cumulative Effects of Mean Temp on Various Mental and Behavior Disorders (The grey

portion of the figures indicates confidence intervals and the red line represents the relative risk estimates. The figure

was produced from model using 4 degrees of freedom for lag and 5 degrees of freedom for temperature. Adjustments

were made for NO2, CO, O3, day of the week and daily humidity levels. Seasonality smoothing was set to 10 per

annum.)

3.4.5 Schizophrenia, Schizotypal and Delusional Disorders (ICD-10 F20-29)

In this group, we observed 46,765 emergency room visits in the city of Toronto during the

study timeframe. At high (99th

percentile) mean temperatures, increases in ER visits were

observed from lags 0 to 7 (Table 3-3). The strongest significant increase in risk occurred at lag 0

71

(RR1.10 95%CI 1.03-1.17). From lag period 1 to 4, the effect was significant (Table 3-3).

Cumulatively, a 249% increase (RR 2.49 95%CI 1.69-3.69) in daily ER visits was observed over

a period of 7 days after initial exposure to high temperatures (Figure 3-3). A clear dose response

relationship was observed with higher ERV increases occurring at higher temperatures (99th

percentile). Interestingly, sensitivity analyses revealed increases in ER visits at cold temperatures

(1st percentile range) when the lag period was altered to 30 days.

3.4.6 Mood Disorders (ICD 10 F30-F39)

Depression, mania, dysthymia, and bipolar disorders are included in this group. We

observed a total of 64,284 emergency room visits in the City of Toronto during the study

timeframe. During high temperatures, increases in ERV were seen from lag 0 to lag 5 (Table 3-

3).The increase is highest at lag 0 and is significant for lags 1 to 3. Overall, a cumulative (7 day)

68 %( RR 1.68 95%CI 1.17-2.40) increase in ER visits was observed after an initial high

temperature day (99th

percentile) (Figure 3-3). No significant associations were observed for cold

temperatures.

3.4.7 Neurotic Disorders (ICD 10 F40-F49)

This category includes various phobia disorders (Agora, Social), anxiety disorders, panic

disorders, stress related disorders, and Obsessive Compulsive disorders. Increases in ER visits

were observed for high temperatures from lag 0 to lag3 (Table 3-3). The strongest effect of high

temperature occurred at lag 1(Table 4). Overall, a 12% (RR 1.12 95%CI 1.00-1.27) increase in

ER visits was seen over a 7-day period after a high ambient temperature day. Also, a 9% (RR

1.09 95%CI 0.84-1.42) increase in ER visits was observed over a week after a cold ambient

temperature day (1st percentile).

72

3.5 Discussion

This is the first ever study to investigate the lagged effects of ambient temperature on

MBDs and specific mental illness ERV. For the population of Toronto, Canada, we found that

exposure to high ambient (99th

percentile) mean temperature is significantly associated with

increases in hospital emergency room visits for individuals with mental and behavior disorders.

We illustrated the lagged structure of these relationships and found that the greatest effect/risk of

emergency room admissions is limited between 0 to 4 lag days for hot temperatures. Age group

stratified analyses revealed that the effect of high temperature on MBDs related ER visits was

greatest among individuals aged 60 and over followed by individuals ages 0-14, 15-39, and 40-

59. No significant differences were seen between genders and no significant cold associations

were observed for overall MBDs.

With respect to specific mental illnesses, significant increases in hospital emergency room

admission during high temperatures were shown for: Schizophrenia and Schizotypal disorders,

Mood disorders, substance abuse related mental and behavior disorders, and Neurotic Disorders.

These effects were restricted to a period of 0 to 7 lag days with peak admissions usually

occurring at lag 0 or lag 1. Individuals with Schizophrenia and Schizotypal disorders and Mood

disorders were affected by high temperatures more than other disease groups. Cold temperature

increases in ER visits were observed for Neurotic and Schizophrenia and Schizotypal disorders.

This was reported after sensitivity analyses with increased lag period indicating the effect of cold

temperature is delayed to a greater extent than high temperatures; however, cold temperature

associations were non-significant.

Although limited direct comparison to previous work can be made since the lag structure

of mental and behavior disorders were not previously investigated, our results are comparable to

some previous work of similar nature indicating that high temperature is significantly associated

73

with increases in emergency room visits for MBDs. In Australia, Khalaj et al. (2010), using a

logistic regression model, have reported increases (7-11%) in emergency hospital admissions

during periods of heat waves for individuals with mental and behavior disorders compared

against individuals without. Other studies, such as Nitschke et al. 2011, Wang et al. 2011,

Nitschke et al. 2007, and Williams et al. 2012, have all reported similar findings between heat

waves and MBDs emergency room visits. Similarly, studies have reported greater risk of hospital

admission with increasing age (Hansen et al. 2008, Nitschke et al. 2011; Wang et al. 2012). For

specific mental and behavior disorders, Hansen et al. 2008 reported significant increases in the

risk of hospital admission after a threshold of 26.7°C for organic (~7.3%), dementia(~17.4%),

psychoactive substance use (~0.5%), schizophrenia (~3.4%), mood disorders (~9.1%), and

neurotic disorders (~9.7%). Although some of these findings were numerically similar to ours,

potential confounders such as various air pollutants (NO2, CO, O3), humidity, and day of the

week were not adjusted for; thus, the extent to which these confounders affect the associations

are unknown and not directly comparable with our results.

Our results for schizophrenia and schizotypal disorders are also similar to studies in other

regions. In Taiwan, Sung et al. 2011 have demonstrated increases in schizophrenia hospital

admissions during periods of change in ambient temperature range. However, no associations

were reported for cold temperatures and no associations were reported for schizoaffective

disorders. In Israel, Shiloh et al. (2000, 2005) reported that mean daily temperature was

significantly correlated with the severity of symptoms and hospital admissions among patients

with schizophrenia. Thus, indicating that temperature may be a risk factor for psychotic

exacerbation in patients with schizophrenia.

74

For Mood disorders, although our illustration of the lag structure of associations between

increased emergency visits and ambient temperature is novel, previous findings have reported

similar correlations. In Egypt, Amr and Volpe (2012) reported data that demonstrated a

correlation between ambient temperature and hospital admissions for affective disorders. In

Brazil, Volpe et al. (2008) and Volpe and Porto (2006) have reported a correlation between

increasing temperature and specific clinical dimensions of mania and hospitalizations for mania.

In Israel, Shiloh et al. (2005) reported a correlation between maximal temperature and admission

rates for bipolar depressed patients. However, no effect estimates were reported; thus, the results

are not directly comparable. Although increases in temperature related mortality have been

reported in individuals with substance abused relate mental illness (Page et al. 2012), increases in

morbidities due to temperature for individuals with this condition has been limited.

Extreme temperatures can exacerbate Mental and Behavior illnesses for various

physiological and behavioral reasons. First, the nature of the psychiatric disorder/illness can

increase an individual’s physiological vulnerability to extreme temperature if specific

neurotransmitters involved in thermoregulation are also involved in the disease process itself

such as in schizophrenia, where impaired dopaminergic transmission have been demonstrated in

animal models (Bark 1998, Sung et al. 2011, Hasegawa et al. 2005). Climatic variables such as

temperature have been previously demonstrated to modulate relevant biological chemicals and its

bioavailability such as plasma tryptophan, serotonin production, brain serotonin turnover, and

platelet serotonin availability, which are potentially related to the psychophysiology of many

affective disorders (Ljubici et al. 2007, Lambert et al. 2002, Maes et al. 1995, Sarrias et al. 1989).

Second, the individual’s illness decreases the ability to remain cognitively aware of the

surrounding environment; thus, neglecting appropriate prevention measures such as drinking

75

extra fluids, taking off clothing when required, and avoiding going outside (Martin-Latry et al.

2007, Bark 1998, Hansen et al. 2008) all of which increase one’ s vulnerability to temperature

modulated psychotic exacerbations. Although the presence of air conditioning is protective

against temperature-related exacerbation of health conditions, individuals with mental and

behavior disorders often tend to have lower socioeconomic status compared with the general

population which could affect their ability to live in an air conditioned home (Semenza 1999).

Finally, individuals with mental illness have higher risks of social isolation and acquiring chronic

cardiovascular and respiratory co-morbidities, all of which are risk factors for increased

vulnerability to the hazardous effects of extreme temperature (McIntyre et al. 2006).

Third, many psychotropic medications have been shown to cause disruption to normal

thermoregulatory mechanisms due their pharmacological properties (Conti et al.2003, Shiloh et

al. 2001, Schwaniger et al. 1998, Yuan et al. 2006). Medications such as anti-cholinergics, anti-

depressants (serotoninergic), anti-histamines (H3), mood stabilizers, anti-psychotics, sedatives,

and anti-epileptics have been associated with abnormal thermoregulation. Anti-cholinergics, anti-

histamines, anti-depressants, and anti-psychotics can lower the elimination of high temperature

through parasympathetic pathways and impairing sweat production. Antipsychotics and

antidepressants may induce elevated body temperatures leading to hyperthermia. Many of these

medications are used in the treatment of mood, stress, anxiety, psychosis, and personality

disorders and when coupled with the nature of their psychiatric illness, behavior symptoms, and

socioeconomic factors can further increase the susceptibility of these individuals to extreme

ambient temperatures.

Dehydration can alter the pharmacokinetics of some of these medications and the side

effects of many psychotropic medications may further add to an individual’s vulnerability to

76

extreme temperature. For individuals with psychoactive substance abuse, numerous substances

may impair physiological responses to extreme temperature. For example, opiates can interfere

with skin vascularization and alter physiological heat responses (Page et al. 2012). Hypnotics

(Alcohol) can cause depression of the central nervous system; thus, inducing dehydration and

haemoconcentration which increases an individual’s vulnerability to heat (Marzuk et al.1998,

Romanovsky and Blatteis 1998).

This study builds on previous literature which identified individuals with psychiatric

illness as a susceptible population to the effects of extreme temperature (hot and cold). However,

our study builds on previous work by investigating these effects with consideration for the lagged

and non-linear nature of these exposure-outcome relationships. To our knowledge this is the first

study in the literature undertaking this endeavor.

Our findings of specific temperature-morbidity relationships are not confounded by

various air pollutants and humidity since adjustments were included for such factors.

Additionally, the results were produced from the models with the best fit after multiple sensitivity

analyses varying the degrees of freedom for the predictor, lag and different time smoothing

functions.

There are some limitations that need to be considered when interpreting the results of our

study. First, the small sample size in a few disease specific categories limited the statistical power

of some disease specific temperature associations and restricted our ability to conduct in-depth

analyses of age and gender per disease subgroup. Second, changes in the diagnostic criteria from

ICD-9 to ICD-10 may have introduced some misclassification among certain diseases. As well, it

is possible that some level of misdiagnosis in the ICD coding could have occurred since many

psychiatric illnesses exhibit similar clinical presentations and psychopathology which can be

77

difficult to distinguish. For example, individuals with bipolar disorder can sometimes be

diagnosed as having schizophrenia since positive symptoms of schizophrenia can resemble manic

episodes while negative symptoms of schizophrenia show resemblance to depressive episodes

(Shiloh et al. 2001).

Moreover, given the wide range of clinical manifestations of psychotic exacerbation, it is

possible some diagnoses were not included in our study due to non-mental health specific ICD

coding. For instance, individuals with mania often exhibit certain dimensions of violence,

aggression, and suicidality; thus, if temperature induced psychotic exacerbation prompted a

manic individual to visit the ER due to physical injuries sustained from acts of violence and

aggression, is possible that the ICD code assigned to the visit would be related to injury rather

than for mania. Also, since mortality data such as suicides were not included in our study, we

were only able to investigate effects on morbidity; thereby, leading to possible underestimation of

the true effects of temperature on mental illness.

Given the lack of individual level data on drug usage/individual medication compliance

and the ecological nature of the study, it was not possible to distinguish between susceptibility

brought on by psychotropic medication usage and/or the presence of a psychiatric illness.

Similarly, the presence of comorbidities and social factors such as financial status, the use of air

conditioning, and whether the individual lived alone were not able to be accounted for in the

study.

Spatial variations, especially in urban settings, are more likely to produce heterogeneous

vulnerabilities to extreme temperature; thus, the usage of aggregated daily average of

temperatures from a network of monitoring stations for a city is likely to result in some

78

“regression dilution” resulting in a potential underestimation of the true effect (Goldberg et al.

2011).

Although we have demonstrated some significant associations between extreme

temperature and mental and behavior disorders, we are unable to pinpoint the exact cause for

them. Future research can build on these limitations by incorporating the usage of medication

history, collection of information on comorbidities and various social indicators (income, air

conditioning, etc.) in order to provide a more thorough understanding of the observed

associations.

3.6 Conclusions

Individuals with mental illness are vulnerable to the effect of extreme temperatures and

preventive measures/adaptation strategies should be developed to minimize the risk of

hospitalization and more serious illnesses. Given that ambient temperature is predicted to rise in

the future, our findings highlight the need for further research to understand the reported

relationships between extreme ambient temperature and mental illnesses in order to mitigate the

risk and reduce the burden of illness on the affected population.

79

3.7 References

1. Allen G., Evaluation of Transformation Methods for Adjustment of Historical TEOM®

Data in the NAPS Network. Environment Canada. 2010

2. American Psychiatric Association (2000). Diagnostic and Statistical Manual of Mental

Disorders (4th

ed., text revision). Washington, DC: American Psychiatric Association.

3. Amr, M. and Volpe, F.M., 2012. Seasonal influences on admissions for mood disorders

and schizophrenia in a teaching psychiatric hospital in Egypt. J. Affect. Disord. 137, 56-

60.

4. Bark, N., 1998. Deaths of psychiatric patients during heat waves. Psychiatr. Serv. 49,

1088-1090.



6. Canadian Institute of Health Information. National Ambulatory Care Reporting System

(NACRS) , 2012.

7. Chong, T.W. and Castle, D.J., 2004. Layer upon layer: thermoregulation in schizophrenia.



Perini, L., 2005. Epidemiologic study of mortality during the Summer 2003 heat wave in




10. Environment Canada. 2011 National Climate Data and Information Archive. Government

of Canada.



12. Gasparrini, A., Armstrong, B., Kenward, M.G., 2010. Distributed lag non-linear models

Stat. Med. 29, 2224-2234.

80




influence of temperature on daily mortality in the temperate climate of Montreal, Canada.

Environ. Res.





heat waves on mental health in a temperate Australian city. Environ. Health Perspect. 116,

1369-1375.




18. Hermesh, H., Shiloh, R., Epstein, Y., Manaim, H., Weizman, A., Munitz, H., 2000. Heat


Am. J. Psychiatry. 157, 1327-1329.

19. Himelhoch, S., Lehman, A., Kreyenbuhl, J., Daumit, G., Brown, C., Dixon, L., 2004.

Prevalence of chronic obstructive pulmonary disease among those with serious mental

illness. Am. J. Psychiatry. 161, 2317-2319.

20. Kaiser, R., Rubin, C.H., Henderson, A.K., Wolfe, M.I., Kieszak, S., Parrott, C.L.,

Adcock, M., 2001. Heat-related death and mental illness during the 1999 Cincinnati heat

wave. Am. J. Forensic Med. Pathol. 22, 303-307.



22. Khalaj, B., Lloyd, G., Sheppeard, V., Dear, K., 2010. The health impacts of heat waves in

five regions of New South Wales, Australia: a case-only analysis. Int. Arch. Occup.

Environ. Health. 83, 833-842.

23. Lambert, G.W., Reid, C., Kaye, D.M., Jennings, G.L., Esler, M.D., 2002. Effect of

sunlight and season on serotonin turnover in the brain. Lancet. 360, 1840-1842.




81

25. Ljubicic, D., Stipcevic, T., Pivac, N., Jakovljevic, M., Muck-Seler, D., 2007. The

influence of daylight exposure on platelet 5-HT levels in patients with major depression

and schizophrenia. J. Photochem. Photobiol. B. 89, 63-69.


114, 227-231.

27. Maes, M., Scharpe, S., Verkerk, R., D'Hondt, P., Peeters, D., Cosyns, P., Thompson, P.,

De Meyer, F., Wauters, A., Neels, H., 1995. Seasonal variation in plasma L-tryptophan

availability in healthy volunteers. Relationships to violent suicide occurrence. Arch. Gen.


28. Martin-Latry, K., Goumy, M.P., Latry, P., Gabinski, C., Begaud, B., Faure, I., Verdoux,

H., 2007. Psychotropic drugs use and risk of heat-related hospitalisation. Eur. Psychiatry.

22, 335-338.

29. Marzuk, P.M., Tardiff, K., Leon, A.C., Hirsch, C.S., Portera, L., Iqbal, M.I., Nock, M.K.,

Hartwell, N., 1998. Ambient temperature and mortality from unintentional cocaine

overdose. JAMA. 279, 1795-1800.

30. McIntyre, R.S., Konarski, J.Z., Soczynska, J.K., Wilkins, K., Panjwani, G., Bouffard, B.,

Bottas, A., Kennedy, S.H., 2006. Medical comorbidity in bipolar disorder: implications

for functional outcomes and health service utilization. Psychiatr. Serv. 57, 1140-1144.









33. Naughton, M.P., Henderson, A., Mirabelli, M.C., Kaiser, R., Wilhelm, J.L., Kieszak,

S.M., Rubin, C.H., McGeehin, M.A., 2002. Heat-related mortality during a 1999 heat

wave in Chicago. Am. J. Prev. Med. 22, 221-227.

34. Nitschke, M., Tucker, G.R., Bi, P., 2007. Morbidity and mortality during heatwaves in

metropolitan Adelaide. Med. J. Aust. 187, 662-665.

35. Nitschke, M., Tucker, G.R., Hansen, A.L., Williams, S., Zhang, Y., Bi, P., 2011. Impact

of two recent extreme heat episodes on morbidity and mortality in Adelaide, South

Australia: a case-series analysis. Environ. Health. 10, 42.

82

36. Nordon, C., Martin-Latry, K., de Roquefeuil, L., Latry, P., Begaud, B., Falissard, B.,

Rouillon, F., Verdoux, H., 2009. Risk of death related to psychotropic drug use in older

people during the European 2003 heatwave: a population-based case-control study. Am. J.

Geriatr. Psychiatry. 17, 1059-1067.

37. Page, L.A., Hajat, S., Kovats, R.S., Howard, L.M., 2012. Temperature-related deaths in

people with psychosis, dementia and substance misuse. Br. J. Psychiatry. 200, 485-490.

38. Postolache, T.T., Mortensen, P.B., Tonelli, L.H., Jiao, X., Frangakis, C., Soriano, J.J.,

Qin, P., 2010. Seasonal spring peaks of suicide in victims with and without prior history

of hospitalization for mood disorders. J. Affect. Disord. 121, 88-93.

39. Radua, J., Pertusa, A., Cardoner, N., 2010. Climatic relationships with specific clinical

subtypes of depression. Psychiatry Res. 175, 217-220.

40. Romanovsky, A.A. and Blatteis, C.M., 1998. Pathophysiology of opioids in hyperthermic

states Prog. Brain Res. 115, 111-127.

41. Sarrias, M.J., Artigas, F., Martinez, E., Gelpi, E., 1989. Seasonal changes of plasma

serotonin and related parameters: correlation with environmental measures. Biol.



Hypothermia induced by atypical neuroleptics Clin. Neuropharmacol. 21, 344-346.

43. Semenza, J.C., Rubin, C.H., Falter, K.H., Selanikio, J.D., Flanders, W.D., Howe, H.L.,

Wilhelm, J.L., 1996. Heat-related deaths during the July 1995 heat wave in Chicago. N.

Engl. J. Med. 335, 84-90.

44. Semenza, J.C., McCullough, J.E., Flanders, W.D., McGeehin, M.A., Lumpkin, J.R., 1999.

Excess hospital admissions during the July 1995 heat wave in Chicago. Am. J. Prev. Med.

16, 269-277.










83

48. Statistics Canada, 2011. Population Census. Government of Canada.

49. Sung, T.I., Chen, M.J., Lin, C.Y., Lung, S.C., Su, H.J., 2011. Relationship between mean

daily ambient temperature range and hospital admissions for schizophrenia: Results from

a national cohort of psychiatric inpatients. Sci. Total Environ. 410-411, 41-46.

50. Turner, L.R., Barnett, A.G., Connell, D., Tong, S., 2012. Ambient temperature and

cardiorespiratory morbidity: a systematic review and meta-analysis. Epidemiology. 23,

594-606.

51. Volpe, F.M. and Del Porto, J.A., 2006. Seasonality of admissions for mania in a

psychiatric hospital of Belo Horizonte, Brazil. J. Affect. Disord. 94, 243-248.

52. Volpe, F.M., Tavares, A., Del Porto, J.A., 2008. Seasonality of three dimensions of

mania: psychosis, aggression and suicidality. J. Affect. Disord. 108, 95-100.




Environ. Med. 69, 163-169.



heat thresholds and temperature relationships. Sci. Total Environ, 414, 126-33.


impact of summer temperatures and heatwaves on mortality and morbidity in Perth.

Australia 1994–2008. Environ. Int. 40, 33-38.

56. World Health Organisation. (1992). ICD-10 Classifications of Mental and Behavioural

Disorder: Clinical Descriptions and Diagnostic Guidelines. Geneva. World Health

Organisation.



28.




316, 1210-1218.

84

Chapter 4

General Discussion and Conclusions

4.1 Summary of Findings

A literature review identified limitations in the way the effects of extreme ambient

temperatures on individuals with mental and behavior disorders have been studied:

-there has been little investigation on the effects of exposure to extreme cold

temperatures,

-the lagged and non-linear nature of these exposure-outcome relationships has not been

incorporated into the statistical analysis, and

-few studies have adjusted for many relevant potential confounders: humidity, various air

pollutants, and day of the week.

A time series study concluded that exposure to heat (99th

percentile of the daily mean

ambient temperature) was associated with a significant increase in ER visits related to mental and

behavior disorders. Similar associations were observed for specific disorders such as

schizophrenia, mood disorders, neurotic disorders, and substance abuse related mental illnesses.

Although there is some indication that exposure to extreme cold temperatures (1st percentile of

the daily mean ambient temperature) may increase ER visits for individuals with schizophrenia

and neurotic disorders, the results are not statistically significant.

4.1 Limitations

Although our time series study was novel in its statistical methodology, limitations related

to exposure and outcome measurements are acknowledged. The methodological issues regarding

exposure measurement largely pertain to the potential underestimation of the actual temperature

85

experienced by the individual. This occurs through a combination of the urban heat island effect,

the location of temperature measurements, and the use of aggregate temperature metrics. These

will be discussed further in depth regarding their specific impacts on the results reported.

4.1.1 Exposure misclassification

Our study used an ecological measure of temperature (daily mean temperature) as the

proxy for the entire geographical region of the study. This approach assumes that all individuals

in the study region will roughly experience the same exposure. However, geographical

differences with respect to temperature vulnerabilities can exist in urban settings (Goldberg et al.

2011). The urban heat island effect is a phenomenon where the positioning of numerous large

urban structures in close proximity can magnify the high temperatures felt by the individual

(Basu and Samet 2002). Numerous factors such as building filtration and air conditioning can

also affect the actual temperature experienced by the individual (Basu and Samet 2002).

The temperature monitoring station used in this study (Toronto Pearson International

Airport) is approximately 28 kilometers from downtown Toronto. This may not represent the

actual temperature conditions experienced in the metropolitan area of Toronto since the

concentration of urban structures is sparser near the airport than in the downtown area; thus,

lowering the urban island effect.

Consequently, some degree of misclassification is inherent. For high temperatures, this

would lead to an underestimation of the actual temperature felt. For cold temperatures, it may be

lead to overestimation of the temperature (colder at the airport than downtown due to fewer

buildings to provide cover for wind-chill effects); however, this is not certain and there is limited

literature to support this speculation. For high temperatures, this underestimation would result in

some regression dilution and would lower the magnitude of the reported associations (Gasparrini

86

and Armstrong 2010). The exact magnitude of this underestimation will largely depend on the

extent of correlation between the study temperature metric and the actual temperature

experienced by the individual. A recently published paper (Goldberg et al. 2011) reported that

the exact magnitude of the underestimation was not significant with respect to the overall risk

estimate for the study. The impact of this exposure measurement error on the association

between cold temperatures and ER visits is unclear since there is very limited literature on the

matter.

While daily mean temperature was the best overall temperature metric to use in our model

as it produced the lowest QAIC value for model fit; the extreme ranges of the daily mean

temperature are lower than the actual minimum and maximum temperature of the day. Although

this difference between the actual temperature extremes and the daily mean temperature extremes

is not significant, this may produce additional misclassification of the exposure and possibly

lowering the associations reported.

Finally, a fixed temperature value may not be suited to represent the exposure since

temperatures often fluctuate throughout the day (Sung et al. 2011). Thus, the daily temperature

range may be the best metric to represent the exposure. Given the limitations of the distributed

non-linear lag modeling in computing estimates based on a range of temperature values, we were

not able to investigate the suitability of using temperature range as the exposure metric.

4.1.2 Outcome misclassification

The methodological issues surrounding outcome measurements are related to the

gathering of morbidity data through NACRS and the use of relevant ICD codes. Hospital ER

visits are currently the standard outcome metric used in measuring morbidity effects in time

series study of temperature. ER visits related to mental and behavior disorders based on ICD-10

87

coding were included in the study. This approach may not be adequate in capturing individuals

with mental illness admitted to the ER due to extreme temperature exposure but coded as seen for

reasons other than mental illness.

For example, when persons with dementia are exposed to extreme heat; however, due to

their impaired cognitive awareness of the surroundings as a result of dementia, they did not take

the appropriate preventive measures (drinking water, remove excessive clothing, use air

conditioning, stay indoors, and etc.). Consequently, they become dehydrated and are admitted to

the ER for treatment of dehydration. These individuals would not be captured under the currently

used method since they were treated for dehydration and not mental illness. This would result in

an underestimation of the morbidity effects as these individuals were not included in the study.

Thus, the ability of the currently used outcome measurements to capture these individual may not

be ideal given that the clinical manifestations of temperature induced outcomes in patients with

mental illness are difficult to predict and can often encompass a wide range of conditions.

For high temperatures, we hypothesize that this limitation in ER visit coding will result in

an underestimation of the actual number of ER visit related to mental and behavior disorders and

lower the magnitude of the relative risk. For cold temperatures, the impact of this limitation is

unclear since the cold temperature associations reported earlier are not statistically significant.

Another issue arises concerning mental and behavior disorders which have similar clinical

presentations that are difficult to distinguish. It is possible that some inherent level of

misdiagnosis between extremely similar conditions will occur. An example using mania and

schizophrenia was given earlier in the previous chapter.

It may be helpful to include ER visits where, although the primary reason for the visit was

not related to mental illness, the individual had an underlying mental illness condition. However,

88

since secondary diagnoses were not mandatory reporting fields under NACRS (CIHI, 2012), the

completeness of this data is questionable. As such, their use would introduce additional

uncertainty and potential for misclassification.

In addition, some non-acute manifestations of temperature-induced health issues

experienced by individuals with mental illness may not be presented in hospital or ER settings.

Instead, they are presented at family physicians and other social workers. Since we did not have

access to this information, the overall morbidity impact of extremes in temperature on individuals

with mental and behavior disorders are underestimated.

4.1.3 Statistical Challenges

Although the use of DLNM in our study was novel since it was able to simultaneously

account for the lagged and non-linear nature of the exposure-outcome relationship, several

limitations exist. First, the great degree of customization regarding model variable selection such

as degrees of freedom, lag, and covariates can lead to unnecessarily complex models which are

time consuming to construct and give marginally little accuracy over simpler models. Second,

although the DLNM is able to adjust for various potential confounders such as air pollutants and

humidity, we are unable to pinpoint exactly how these covariates influence the magnitude of the

relative risk. Also, the use of lag periods for covariates without caution may decrease the

reliability of the results since the model does not account for the interaction of these time

dependent exposure-outcome relationships. Third, given that the casual pathway for temperature-

exacerbated health outcomes are often complex and may involve many synergic pathways with

air pollutants and humidity, the ability to investigate interactive effects between temperature, air

pollutants, humidity, and the outcome variable is crucial. However, the ability of the DLNM to

investigate interactive effects is limited given the rigid programing barriers in this regard.

89

4.2 Public Health Implications

Individuals with mental illness are vulnerable to the effect of extreme temperatures. The

vulnerabilities differ with respect to age group and specific mental and behavior disorders. From

a public health perspective, meaningful interventions, preventive measures, and adaptation

strategies should be employed to minimize the risk of hospitalization and more serious illnesses

where opportunity arises.

Given some individuals with mental and behavior disorders will be unaware of the risks

from extreme temperature exposure, adequate patient counseling and education regarding

information on exposure reduction and preventive measures such as intake of sufficient fluids,

wearing appropriate clothing, taking showers, avoiding going out during periods of extreme heat

or cold, use of air conditioning, and the use of heat shelters should be incorporated into outpatient

care programs.

Issuing heat/cold alerts to the public and relevant health authorities, and the formulation

of heat-wave/cold-spell response plans are needed. The establishment of early heat warning

systems is also important since they often have lead times of 12 to 48 hours which offer valuable

time for preparation to decision makers and public health practitioners (Basu and Samet 2002). In

Europe after the heat waves in 2003, many early heat warning systems were implemented in 16

European countries to provide relevant officials time to prepare before heat waves (Basu and

Samet 2002). However, no conclusion can be drawn regarding their effectiveness since they have

not been evaluated.

Although not directly examined in our study, but given the relevant biological link, health

care practitioners working with individuals with mental and behavior disorders should be trained

to evaluate risk and apply interventions such as monitoring of indoor temperature during relevant

months, conducting face to face interviews during periods of elevated temperature to check for

90

symptoms of dehydration and other temperature-induced illnesses, and, when possible, switching

medications that affect thermoregulation for alternative formulations that do not.

The public health measures listed above should be studied to demonstrate how they can

contribute to improvements in the management and care of the individuals with mental and

behavior disorders. Their ability to reduce the economic and social costs of extreme temperature

events must also be demonstrated.

4.3 References



91

2. Canadian Institute of Health Information. National Ambulatory Care Reporting System

(NACRS), 2012.




Stat. Med. 29, 2224-2234.




influence of temperature on daily mortality in the temperate climate of Montreal. Canada

Environ. Res. 111,853-860.

7. Sung, T.I., Chen, M.J., Lin, C.Y., Lung, S.C., Su, H.J., 2011. Relationship between mean

daily ambient temperature range and hospital admissions for schizophrenia: Results from

a national cohort of psychiatric inpatients. Sci. Total Environ. 410-411, 41-46.

92

4.4 Appendices

4.4.1 Appendix A: Ethics Certificate

93

4.4.2 Appendix B: Example of Time Series Dataset, ER Visit Distribution over a year, and Temperature Distribution over a year

Example of Time series Dataset

date MBD Year Month TIME Day MEANT RELHUM DOW AVGNO2 AVGCO AVGO3 AVGSO2 AVGPM25

1/1/2005 110 2005 1 1007 1 1.3 67.5 Saturday 11.2 0.4 14.8 0 2.4

1/2/2005 79 2005 1 1008 2 2.5 90.29 Sunday 15.2 0.6 13.6 1 4.2

1/3/2005 86 2005 1 1009 3 2.1 88.67 Monday 25.8 0.65 4.2 1 4.2

1/4/2005 93 2005 1 1010 4 -0.2 79.83 Tuesday 22.6 0.55 12.4 0.5 3.7

1/5/2005 103 2005 1 1011 5 -3.7 69.46 Wednesday 19.6 0.45 16.2 0.5 1.5

1/6/2005 90 2005 1 1012 6 -4.4 78.21 Thursday 20 0.5 16 1 3

1/7/2005 98 2005 1 1013 7 -3 72.96 Friday 26.2 0.45 17.4 2.5 5.2

1/8/2005 91 2005 1 1014 8 -1.1 80.5 Saturday 29.2 0.65 9.2 3.5 10.2

1/9/2005 88 2005 1 1015 9 -0.2 84.167 Sunday 25.2 0.6 8.6 5.5 12.8

1/10/2005 113 2005 1 1016 10 0.3 73.46 Monday 20.2 0.55 12.8 3 7

1/11/2005 99 2005 1 1017 11 -2.7 72.54 Tuesday 21.4 0.55 17 1 3.2

1/12/2005 96 2005 1 1018 12 1.4 91.58 Wednesday 23 0.55 8.6 1 5.2

1/13/2005 85 2005 1 1019 13 9 87.08 Thursday 25.4 0.3 10.8 4 7.5

1/14/2005 83 2005 1 1020 14 -4.6 71.92 Friday 16.4 0.2 25.4 0 3.7

1/15/2005 83 2005 1 1021 15 -9.1 67.25 Saturday 15.2 0.25 25.2 1.5 4

1/16/2005 72 2005 1 1022 16 -9.3 75.21 Sunday 24.2 0.4 15.6 1 4.2

1/17/2005 101 2005 1 1023 17 -13.5 75.08 Monday 25 0.35 16.4 1.5 3.8

1/18/2005 90 2005 1 1024 18 -15.6 60.54 Tuesday 20.6 0.3 20.2 3.5 5.5

1/19/2005 90 2005 1 1025 19 -5.5 78.96 Wednesday 26 0.3 13 10.5 6.8

1/20/2005 85 2005 1 1026 20 -14.4 71.08 Thursday 19.2 0.3 22.2 0 2.3

MDB =daily counts of ER visits related to mental and behavior disorders, MEANT= Daily mean temperature, RELHUM= Daily Humidity

concentration, AVGNO2= Daily NO2 concentration levels, AVGCO= daily CO concentration, AVGO3= daily ozone concentration, AVGSO2=

daily SO2 concentration, AVGPM2.5 = daily particular matter concentration, and DOW = day of the week, TIME= Number of days of Study.

94

ER visits pattern for 2005

0

20

40

60

80

100

120

140

-35 15 65 115 165 215 265 315 365

Nu

mb

er

of

Dai

ly E

R V

isit

sed

re

late

d t

o

Me

nta

l an

d B

eh

vaio

r D

iso

rde

rs

Days of the year

Mental and Behvaior Admissions Patterns (2005)

95

Daily Mean Temperature Patterns for 2005

-30

-20

-10

0

10

20

30

40

-35 15 65 115 165 215 265 315 365

Me

an D

aily

Te

mp

era

ture

(°C

)

Number of Days

Daily Mean Temperature Patterns (2005)

MEANT

96

4.4.3 Appendix C: Statistical Model Equation

The equation for the quasi-Poisson regression will be:

Log [ut] = α +β BS [Tt, lag30], + [NO2, lag2], + [CO, lag1] + [O3, lag2] + nDOW +

kHumidity,

Where α is the equation intercept, β is the vector of the coefficients for the Tt, and lag

corresponds to the lag period in which the effect is investigated over. Furthermore,

adjustments based day of the week (DOW) and humidity will be done.

(TITLE OF THE THESIS)* Profiles/Wang_Xiang... · 2018-07-14 · 1.2 Economic Burden of Mental...

Documents

Transcript of (TITLE OF THE THESIS)* Profiles/Wang_Xiang... · 2018-07-14 · 1.2 Economic Burden of Mental...